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The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue titled "Machine Learning in Computer Vision and Image Sensing: Theory and Applications." The editorial highlights the diverse applications of machine learning (ML) models in various domains such as medical imaging, signal processing, remote sensing, and human activity detection. The special issue includes 11 papers that cover topics such as image segmentation, fluvial navigation, Alzheimer's disease classification, pneumothorax detection, lung cancer malignancy prediction, amniotic fluid volume detection, COVID-19 detection, and Parkinson's disease detection. The papers showcase the progress and potential of ML models in computer vision applications and provide valuable insights for future research. [Extracted from the article]

Prostate cancer is a commonly diagnosed cancerous disease among men world-wide. Even with modern technology such as multi-parametric magnetic resonance tomography and guided biopsies, the process for diagnosing prostate cancer remains time consuming and requires highly trained professionals. In this paper, different convolutional neural networks (CNN) are evaluated on their abilities to reliably classify whether an MRI sequence contains malignant lesions. Implementations of a ResNet, a ConvNet and a ConvNeXt for 3D image data are trained and evaluated. The models are trained using different data augmentation techniques, learning rates, and optimizers. The data is taken from a private dataset, provided by Cantonal Hospital Aarau. The best result was achieved with a ResNet3D, yielding an average precision score of 0.4583 and AUC ROC score of 0.6214. [ABSTRACT FROM AUTHOR]

Wild flowers and plants play an important role in protecting biodiversity and providing various ecosystem services. However, some of them are endangered or threatened and are entitled to preservation and protection. This study represents a first step to develop a computer vision system and a supporting mobile app for detecting and monitoring the development stages of wild flowers and plants, aiming to contribute to their preservation. It first introduces the related concepts. Then, surveys related work and categorizes existing solutions presenting their key features, strengths, and limitations. The most promising solutions and techniques are identified. Insights on open issues and research directions in the topic are also provided. This paper paves the way to a wider adoption of recent results in computer vision techniques in this field and for the proposal of a mobile application that uses YOLO convolutional neural networks to detect the stages of development of wild flowers and plants. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification. [ABSTRACT FROM AUTHOR]

Aided image diagnostics (CAD) have been used in many fields of diagnostic medicine. It relies heavily on classical computer vision and artificial intelligence. Quantum neural network (QNN) has been introduced by many researchers around the world and presented recently by research corporations such as Microsoft, Google, and IBM. In this paper, the investigation of the validity of using the QNN algorithm for machine-based breast cancer detection was performed. To validate the learnability of the QNN, a series of learnability tests were performed alongside with classical convolutional neural network (CCNN). QNN is built using the Cirq library to perform the assimilation of quantum computation on classical computers. Series of investigations were performed to study the learnability characteristics of QNN and CCNN under the same computational conditions. The comparison was performed for real Mammogram data sets. The investigations showed success in terms of recognizing the data and training. Our work shows better performance of QNN in terms of successfully training and producing a valid model for smaller data set compared to CCNN. [ABSTRACT FROM AUTHOR]

Waste disposal remains a challenge due to land availability, and environmental and health issues related to the main disposal method, landfilling. Combining computer vision (machine learning) and robotics to sort waste is a cost-effective solution for landfilling activities limitation. The objective of this study was to combine transfer and ensemble learning to process collected waste images and classify landfill waste into nine classes. Pretrained CNN models (Inception–ResNet-v2, EfficientNetb3, and DenseNet201) were used as base models to develop the ensemble network, and three other single CNN models (Models 1, 2, and 3). The single network performances were compared to the ensemble model. The waste dataset, initially grouped in two classes, was obtained from Kaggle, and reorganized into nine classes. Classes with a low number of data were improved by downloading additional images from Google search. The Ensemble Model showed the highest prediction precision (90%) compared to the precision of Models 1, 2, and 3, 86%, 87%, and 88%, respectively. All models had difficulties predicting overlapping classes, such as glass and plastics, and wood and paper/cardboard. The environmental costs for the Ensemble network, and Models 2 and 3, approximately 15 g CO2 equivalent per training, were lower than the 19.23 g CO2 equivalent per training for Model 1. [ABSTRACT FROM AUTHOR]

Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately reconstruct, recognize, and locate potholes. In recent years, various methods utilizing (a) computer vision, (b) three-dimensional (3D) point clouds, or (c) smartphone data have been employed to map road surface quality conditions. Machine learning and deep learning techniques have increasingly enhanced the performance of these methods. This review aims to provide a comprehensive overview of cutting-edge computer vision and machine learning algorithms for pothole detection. It covers topics such as sensing systems for acquiring two-dimensional (2D) and 3D road data, classical algorithms based on 2D image processing, segmentation-based algorithms using 3D point cloud modeling, machine learning, deep learning algorithms, and hybrid approaches. The review highlights that hybrid methods combining traditional image processing and advanced machine learning techniques offer the highest accuracy in pothole detection. Machine learning approaches, particularly deep learning, demonstrate superior adaptability and detection rates, while traditional 2D and 3D methods provide valuable baseline techniques. By reviewing and evaluating existing vision-based methods, this paper clarifies the current landscape of pothole detection technologies and identifies opportunities for future research and development. Additionally, insights provided by this review can inform the design and implementation of more robust and effective systems for automated road surface condition assessment, thereby contributing to enhanced roadway safety and infrastructure management. [ABSTRACT FROM AUTHOR]

Rail surface defects typically serve as early indicators of railway malfunctions, which may compromise the quality and corrosion resistance of rails, thereby endangering the safe operation of trains. The timely detection of defects is essential to ensure the safe operation of railways. To improve the classification accuracy of rail surface defect detection, this paper proposes a rail surface defects detection algorithm based on MobileNet-YOLOv7. By integrating lightweight deep learning algorithms into the engineering application of rail surface defect detection, a MobileNetV3 lightweight network is used as the backbone network for YOLOv7 to enhance both speed and accuracy in complex defect extraction. Subsequently, the efficient intersection over union (EIOU) loss function is utilized as the positional loss function to bolster system resilience. Finally, the k-means++ clustering algorithm is applied to obtain new anchor boxes. The experimental results demonstrate the effectiveness of the proposed method, achieving superior detection accuracy compared with traditional algorithms. [ABSTRACT FROM AUTHOR]

Semantic segmentation is an essential process in computer vision that allows users to differentiate objects of interest from the background of an image by assigning labels to the image pixels. While Convolutional Neural Networks have been widely used to solve the image segmentation problem, simpler approaches have recently been explored, especially in fields where explainability is essential, such as medicine. A Convolutional Decision Tree (CDT) is a machine learning model for image segmentation. Its graphical structure and simplicity make it easy to interpret, as it clearly shows how pixels in an image are classified in an image segmentation task. This paper proposes new approaches for inducing a CDT to solve the image segmentation problem using SHADE. This adaptive differential evolution algorithm uses a historical memory of successful parameters to guide the optimization process. Experiments were performed using the Weizmann Horse dataset and Blood detection in dark-field microscopy images to compare the proposals in this article with previous results obtained through the traditional differential evolution process. [ABSTRACT FROM AUTHOR]

The paper provides an overview of current methods for recognizing and classifying visual images in static images or video stream. The paper will discuss various approaches, including machine learning, current problems of these methods and possible improvements. The biggest challenges of the visual image retrieval and classification task are discussed. The main emphasis is placed on the review of such promising algorithms as SSD, YOLO, R-CNN, an overview of the principles of these methods, network architectures. Objective. The aim of the work is to analyze existing studies and find the best algorithm for recognizing and classifying visual images for further activities. Method. Primary method is to compare different factors of algorithms in order to select the most perspective one. There are different marks to compare, like image processing speed, accuracy. There are a number of studies and publications that propose methods and algorithms for solving the problem of finding and classifying images in an image [3–6]. It should be noted that most promising approaches are based on machine learning methods. It is worth noting that the proposed methods have drawbacks due to the imperfect implementation of the Faster R-CNN, YOLO, SSD algorithms for working with streaming video. The impact of these drawbacks can be significantly reduced by applying the following solutions: development of combined identification methods, processing of edge cases – tracking the position of identified objects, using the difference between video frames, additional preliminary preparation of input data. Another major area for improvement is the optimization of methods to work with real-time video data, as most current methods focus on images. Results. As an outcome of the current research we have found an optimal algorithm for further researches and optimizations. Conclusions. Analysis of existent papers and researches has demonstrated the most promising algorithm for further optimizations and experiments. Also current approaches still have some space for further. The next step is to take the chosen algorithm and investigate possibilities to enhance it. [ABSTRACT FROM AUTHOR]

License Plate Recognition (LPR) is one of the tough tasks in the field of computer vision. Although it has been around for quite a while, there still lies the challenges when we have to deal with; the harsh environmental conditions like snowy, rainfall, windy, low light conditions etc. as well as the condition of the plates which includes the bent, rotated, broken plates. The performance of the recognition and detection frameworks take a significant hit when it is concerned with these conditional effects on the license plate. In this paper, we introduced a model to improve our accuracy based on the Chinese Car Parking Dataset (CCPD) using 2 separate convolutional neural networks. The first CNN will be able to detect the bounding boxes for the license plate detection using Non-Maximus Suppression (NMS) to find the most probable bounding area whereas the second one will take these bounding boxes and use the spatial attenuation network and character recognition model to successfully recognize the license plate. First, we train the CNN to detect the license plates, then use the second CNN to recognize the characters. The overall recognition accuracy was found to be 89% in the CCPD dataset. [ABSTRACT FROM AUTHOR]

Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and large-scale farming, the need for a low-cost ML-enabled framework is more pressing. In this paper, we present an end-to-end solution that utilizes tiny ML (TinyML) for the low-cost adoption of ML in classification tasks with a focus on the post-harvest process of olive fruits. We performed dataset collection to build a dataset that consists of several varieties of olive fruits, with the aim of automating the classification and sorting of these fruits. We employed simple image segmentation techniques by means of morphological segmentation to create a dataset that consists of more than 16,500 individually labeled fruits. Then, a convolutional neural network (CNN) was trained on this dataset to classify the quality and category of the fruits, thereby enhancing the efficiency of the olive post-harvesting process. The goal of this study is to show the feasibility of compressing ML models into low-cost edge devices with computationally constrained settings for tasks like olive fruit classification. The trained CNN was efficiently compressed to fit into a low-cost edge controller, maintaining a small model size suitable for edge computing. The performance of this CNN model on the edge device, focusing on metrics like inference time and memory requirements, demonstrated its feasibility with an accuracy of classification of more than 97.0 % and minimal edge inference delays ranging from 6 to 55 inferences per second. In summary, the results of this study present a framework that is feasible and efficient for compressing CNN models on edge devices, which can be utilized and expanded in many agricultural applications and also show the practical insights for implementing the used CNN architectures into edge IoT devices and show the trade-offs for employing them using TinyML. [ABSTRACT FROM AUTHOR]

Single image super‐resolution (SISR) is a promising research direction in computer vision and image processing for improving the visual perception of low‐quality images. In recent years, deep learning algorithms have driven tremendous development in SR, and SR methods based on various network architectures have significantly improved the quality of reconstructed images. Although there has been a large amount of reviews focusing on SISR, few studies have focused specifically on network architectures for SISR. This paper aims to provide a systematic overview of the design ideas of SISR using multiple architectures, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Transformer, and Diffusion model. In addition, an experimental analysis and comparison of state‐of‐the‐art SR algorithms have been performed on publicly available quantitative and qualitative datasets. Finally, some future directions are discussed that may help other community researchers. [ABSTRACT FROM AUTHOR]

Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation—identifying crops from the background—crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40 % higher Intersection-over-Union (IoU) than the threshold method and 11 % over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code. [ABSTRACT FROM AUTHOR]

This paper proposes a novel deep learning-based approach for financial chart patterns classification. Convolutional neural networks (CNNs) have made notable achievements in image recognition and computer vision applications. These networks are usually based on two-dimensional convolutional neural networks (2D CNNs). In this paper, we describe the design and implementation of one-dimensional convolutional neural networks (1D CNNs) for the classification of chart patterns from financial time series. The proposed 1D CNN model is compared against support vector machine, extreme learning machine, long short-term memory, rule-based and dynamic time warping. Experimental results on synthetic datasets reveal that the accuracy of 1D CNN is highest among all the methods evaluated. Results on real datasets also reveal that chart patterns identified by 1D CNN are also the most recognized instances when they are compared to those classified by other methods. [ABSTRACT FROM AUTHOR]

In the Brazilian state of São Paulo, contaminated sites (CSs) constitute threats to health, environment and socioeconomic situation of populations. Over the past two decades, the Environmental Agency of São Paulo (CETESB) has monitored these known CSs. This paper discusses the produced dataset through digitising the CETESB reports and making them accessible to the public in English. The dataset reports on qualitative aspects of contamination within the registered sites (e.g., contamination type and spread) and their management status. The data was extracted from CETESB reports using a machine-learning computer vision algorithm. It comprises two components: an optical character recognition (OCR) engine for text extraction and a convolutional neural network (CNN) image classifier to identify checked boxes. The digitisation was followed by harmonisation and quality assurance processes to ensure the consistency and validity of the data. Making this dataset accessible will allow future work on predictive analysis and decision-making and will inform the required policy-making to improve the management of the CSs in Brazil. [ABSTRACT FROM AUTHOR]

Scoring targets in shooting sports is a crucial and time-consuming task that relies on manually counting bullet holes. This paper introduces an automatic score detection model using object detection techniques. The study contributes to the field of computer vision by comparing the performance of seven models (belonging to two different architectural setups) and by making the dataset publicly available. Another value-added aspect is the inclusion of three variants of the object detection model, YOLOv8, recently released in 2023 (at the time of writing). Five of the used models are single-shot detectors, while two belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640 × 640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores. Among these models, YOLOv8m performed the best, with the highest mAP50 value of 96.7%, followed by the performance of YOLOv8s with the mAP50 value of 96.5%. It is suggested that if the system is to be implemented in a real-time environment, YOLOv8s is a better choice since it took significantly less inference time (2.3 ms) than YOLOv8m (5.7 ms) and yet generated a competitive mAP50 of 96.5%. [ABSTRACT FROM AUTHOR]

In addition, this paper also discussed how tabular data sets are converted into images for the application of two-dimensional convolutional neural networks, and how black box models using local and global rule extractions and rule-based methods affect reliability. To improve this, the contribution by Yoo and Kim, "Merchant Recommender System Using Credit Card Payment Data", proposed a model specialized for affiliate store recommendations by reflecting the domain information of credit card payment data [[11]]. Artificial intelligence technology has become an indispensable element in the field of electronics, and its influence is enormous. The multi-model inference accelerator was tested on low-density Zynq-7010 and Zynq-7020 FPGA devices and image classification experiments were performed on the CIFAR-10 dataset. [Extracted from the article]

This paper focuses on detecting leaf diseases in wheat plants from the beginning to the end of the plant's life cycle. It highlights the best techniques for detecting various types of wheat leaf diseases and emphasizes the use of computer vision, image processing, and machine learning. The main focus is on classifying these diseases through deep convolutional neural networks, a popular image recognition and classification approach. The paper reviews various techniques for classifying image-based wheat leaf diseases, including spot blotch, stripe rust, brown rust, and powdery mildew. The paper aims to summarize the state-of-theart techniques for detecting wheat leaf diseases. [ABSTRACT FROM AUTHOR]

In India and over the world, Cancer has become a deadly disease that is adversely affecting women from different age groups. According to a survey, one in every 30 women suffers from this deadly disease in their lifetime. This project was first thought of because of the increase in cases of breast cancer and if we can detect it at an early stage then there is an increased chance of it getting cured. This paper lays a foundation in making the detection of cancer automated from histopathological images using deep learning algorithms so that more and more people can get it diagnosed at an early stage and subsequently get cured. To enhance the capability of histopathology image classification, a powerful deep learning algorithm and a sizable, varied dataset are required. In this paper, we have proposed an automatic and precise histopathological image analytical method, to detect breast cancer at an early stage. Deep learning techniques have recently made significant strides and produced outstanding results in the fields of computer vision and image processing, which has encouraged many researchers to use this method for the categorization of histopathology images. [ABSTRACT FROM AUTHOR]

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual 'fingerprints' that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data. Author summary: Since the birth of microbiology, scientists have looked at the patterns of bacterial growth on agar (colony morphology) as a key tool for identifying bacterial species. We return to this traditional approach with modern tools of computer vision and deep learning and show that we can achieve high levels of classification accuracy on a within-species scale, despite what is considered a 'data-starved' dataset. Our results show that strains of the environmental generalist and opportunistic pathogen Pseudomonas aeruginosa have a characteristic morphological 'fingerprint' that enables accurate strain classification via a custom deep convolutional neural network. Our work points to extensions towards predicting phenotypes of interest (e.g. antibiotic resistance, virulence), and suggests that sample size limitations may be less restrictive than previously thought for deep learning applications in biology, given appropriate use of data augmentation and transfer-learning tools. [ABSTRACT FROM AUTHOR]

The presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods. Each of the proposed feature descriptors outputs a feature activation map in the form of a grayscale image. Acquired feature maps can be appended in a straightforward way to the original color channels of the input image and passed to the input of a convolutional neural network during the training and inference steps. An experimental evaluation is conducted to compare the classification ROC AUC of feature-extended convolutional neural network models with baseline models using regular color image inputs. The advantage of feature-extended models is demonstrated for the Resnet and VGG convolutional neural network architectures. [ABSTRACT FROM AUTHOR]

Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption compared to state-of-the-art deep learning algorithms. Our proposed system maintains two steps. At first, this work extracted the local features of an image by using a local binary pattern descriptor, and then we used deep learning to extract global features. The proposed approach has achieved excellent accuracy and high performance. The performance of the proposed method was tested on three benchmark datasets: the realworld masked faces dataset (RMFD), the simulated masked faces dataset (SMFD), and labeled faces in the wild (LFW). Performancemetrics for the proposed technique weremeasured in terms of accuracy, precision, recall, and F1-score. Results indicated the efficiency of the proposed technique, providing accuracies of 99.86%, 99.98%, and 100% for RMFD, SMFD, and LFW, respectively. Moreover, the proposed method outperformed state-of-the-art deep learningmethods in the recent bibliography for the same problem under study and on the same evaluation datasets. [ABSTRACT FROM AUTHOR]

Deep learning has gained significant popularity due to its exceptional performance in various machine learning and artificial intelligence applications. In this paper, we propose a comprehensive methodology for enhancing leaf disease detection using Convolutional Neural Networks (CNNs). Our approach leverages the power of CNNs and introduces innovative techniques to improve accuracy and provide insights into the inner workings of the models. The methodology encompasses multiple stages. We describe the methodology as follows: Firstly, we employ advanced preprocessing techniques to enhance the leaf image dataset, including data augmentation methods to augment the training data and improve model accuracy. Secondly, we design and implement a robust Convolutional Neural Network architecture with multiple layers and ReLU activation, enabling the network to effectively learn complex patterns and features from the input images. To facilitate monitoring and control of the CNN processes, we introduce a novel network visualization module. This module offers a filter-level 2D embedding view, providing real-time insights into the inner workings of the network and aiding in the interpretation of the learned features. Additionally, we develop an interactive module that enables real-time model control, allowing researchers and practitioners to fine-tune the model parameters and optimize its performance. To evaluate the effectiveness of our proposed methodology, we conduct extensive experiments using the PlantVillage dataset, which contains a diverse range of plant diseases captured through a large number of leaf images. Through rigorous analysis and evaluation, we demonstrate the superior performance of our approach, achieving classification accuracy exceeding 99%. [ABSTRACT FROM AUTHOR]

The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of a solution for detecting the machining defects from an engine block, in the piston chamber. The solution was developed for an automotive manufacturer and the main goal of the implementation is the replacement of the visual inspection performed by a human operator with a computer vision application. We started by exploring different machine vision applications used in the manufacturing environment for several types of operations, and how machine learning is being used in robotic industrial applications. The solution implementation is re-using hardware that is already available at the manufacturing plant and decommissioned from another system. The re-used components are the cameras, the IO (Input/Output) Ethernet module, sensors, cables, and other accessories. The hardware will be used in the acquisition of the images, and for processing, a new system will be implemented with a human–machine interface, user controls, and communication with the main production line. Main results and conclusions highlight the efficiency of the CCD (charged-coupled device) sensors in the manufacturing environment and the robustness of the machine learning algorithms (convolutional neural networks) implemented in computer vision applications (thresholding and regions of interest). [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has made enormous strides in recent years, transitioning from science fiction to a technology that is revolutionizing every sector of the global economy. Thanks to advances in machine learning, natural language processing and computer vision, artificial intelligence is no longer a futuristic dream, but a reality today. Once optimised and integrated into everyday life, artificial intelligence will substantially enhance human capabilities and contribute to the betterment of society. This paper will embrace the opportunities offered by computer vision, as part of artificial intelligence, by showcasing the FurnishMe application, an image search engine for furniture recommendations. Buying furniture online, as well as offline, is an overwhelming process given the quantity and diversity of furniture products available. The FurnishMe software solution allows users to easily explore the furniture of the three largest furniture retailers in Romania: Ikea, Jysk, and Dedeman. The system analyses user-uploaded interior design images in order to identify furniture items and provide aesthetically similar products from the three big retailers mentioned above. Both consumers and traders benefit from this solution. Clients benefit from a quick and easy way to choose the product they desire which integrates various features such as design, texture and colour. Moreover, businesses gain from greater sales by luring clients and saving time on inperson consultations. [ABSTRACT FROM AUTHOR]

Advancements in Machine Learning (ML) and Computer Vision have led to notable improvements in the detection of breast cancer. However, the accuracy of the classifier is limited due to imbalanced datasets that cause overfitting. Thus, additional images are needed to improve the classifier's performance. Generative Adversarial Networks (GAN) are used for image augmentation. Still, its limitations, such as the random use of different types of GAN, can lead to a too-good discriminator, causing generator training to fail due to vanishing gradients. Therefore, selecting the appropriate GAN model for the given scenario is crucial for optimal performance. This paper proposes a novel Smart Generative Adversarial Network (Smart GAN) architecture to develop an efficient and computational classification model for a limited imbalanced dataset. Smart GAN uses a three-fold approach. Different types of GAN augment the dataset in the first phase of experimental work, and their evaluation metrics are calculated. In the second phase, the metric scores are used as rewards for the Reinforcement learning model (Q-learning approach). The best augmentation is chosen based on the best Q-values for each metric score. It compares three different Convolutional Neural Networks (CNN) and selects the best-suited network to classify the augmented datasets. The proposed Smart GAN architecture outperforms other existing approaches by giving a better accuracy of 89.62% and 89.91% on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) augmented datasets, respectively, representing an approximately 10% increment in detection rate compared to non-augmented datasets. [ABSTRACT FROM AUTHOR]

In recent years, video surveillance has become an integral part of computer vision research, addressing a variety of challenges in security, memory management and content extraction from video sequences. This paper introduces the Robust Object Detection using Fire Hawks Optimizer with Deep Learning (ROD-FHODL) technique, a novel approach designed specifically for video surveillance applications. Combining object detection and classification the proposed technique employs a two-step procedure. Utilizing the power of the Mask Region-based Convolutional Neural Network (Mask-RCNN) for object detection, we optimize its hyperparameters using the Fire Hawks Optimizer (FHO) algorithm to improve its efficacy. Our experimental results on the UCSD dataset demonstrate the significant impact of the proposed work. It achieves an extraordinary RUNNT of 1.34 s on the pedestrian-1 dataset, significantly outperforming existing models. In addition, the proposed system surpasses in accuracy, with a pedestrian-1 accuracy rate of 97.45% and Area Under the Curve (AUC) values of 98.92%. Comparative analysis demonstrates the superiority of the proposed system in True Positive Rate (TPR) versus False Positive Rate (FPR) across thresholds. In conclusion, the proposed system represents a significant advancement in video surveillance, offering advances in speed, precision and robustness that hold promise for enhancing security, traffic management and public space monitoring in smart city infrastructure and other applications. [ABSTRACT FROM AUTHOR]

The Oddy test is an accelerated ageing test used to determine whether a material is appropriate for the storage, transport, or display of museum objects. The levels of corrosion seen on coupons of silver, copper, and lead indicate the material's safety for use. Although the Oddy test is conducted in heritage institutions around the world, it is often critiqued for a lack of repeatability. Determining the level of corrosion is a manual and subjective process, in which outcomes are affected by differences in individuals' perceptions and practices. This paper proposes that a more objective evaluation can be obtained by utilising a convolutional neural network (CNN) to locate the metal coupons and classify their corrosion levels. Images provided by the Metropolitan Museum of Art (the Met) were labelled for object detection and used to train a CNN. The CNN correctly identified the metal type and corrosion level of 98% of the coupons in a test set of the Met's images. Images were also collected from the American Institute for Conservation's Oddy test wiki page. These images suffered from low image quality and were missing the classification information needed to train the CNN. Experts from cultural heritage institutions evaluated the coupons in the images, but there was a high level of disagreement between expert classifications. Therefore, these images were not used to train the CNN. However, the images proved useful in testing the limitations of the CNN trained on the Met's data when applied to images of coupons from different Oddy test protocols and photo documentation procedures. This paper presents the effectiveness of the CNN trained on the Met's data to classify Met and non-Met Oddy test coupons. Finally, this paper proposes the next steps needed to produce a universal CNN-based classification tool. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose $\Pi$ Π -Nets, a new class of function approximators based on polynomial expansions. $\Pi$ Π -Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. The unknown parameters, which are naturally represented by high-order tensors, are estimated through a collective tensor factorization with factors sharing. We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks. We empirically demonstrate that $\Pi$ Π -Nets are very expressive and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, $\Pi$ Π -Nets produce state-of-the-art results in three challenging tasks, i.e., image generation, face verification and 3D mesh representation learning. The source code is available at https://github.com/grigorisg9gr/polynomial_nets. [ABSTRACT FROM AUTHOR]

Researchers can investigate many aspects of animal ecology through noninvasive photo–identification. Photo–identification is becoming more efficient as matching individuals between photos is increasingly automated. However, the convolutional neural network models that have facilitated this change need many training images to generalize well. As a result, they have often been developed for individual species that meet this threshold. These single‐species methods might underperform, as they ignore potential similarities in identifying characteristics and the photo–identification process among species.In this paper, we introduce a multi‐species photo–identification model based on a state‐of‐the‐art method in human facial recognition, the ArcFace classification head. Our model uses two such heads to jointly classify species and identities, allowing species to share information and parameters within the network. As a demonstration, we trained this model with 50,796 images from 39 catalogues of 24 cetacean species, evaluating its predictive performance on 21,192 test images from the same catalogues. We further evaluated its predictive performance with two external catalogues entirely composed of identities that the model did not see during training.The model achieved a mean average precision (MAP) of 0.869 on the test set. Of these, 10 catalogues representing seven species achieved a MAP score over 0.95. For some species, there was notable variation in performance among catalogues, largely explained by variation in photo quality. Finally, the model appeared to generalize well, with the two external catalogues scoring similarly to their species' counterparts in the larger test set.From our cetacean application, we provide a list of recommendations for potential users of this model, focusing on those with cetacean photo–identification catalogues. For example, users with high quality images of animals identified by dorsal nicks and notches should expect near optimal performance. Users can expect decreasing performance for catalogues with higher proportions of indistinct individuals or poor quality photos. Finally, we note that this model is currently freely available as code in a GitHub repository and as a graphical user interface, with additional functionality for collaborative data management, via Happywhale.com. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) refers to the implementation of a damage detection strategy for structures. Fault occurrence in these structural systems during the operation is inevitable. Efficient, fast, and precise health monitoring methods are required to proactively perform the necessary repairs and maintenance on time before it is too late. The current structural health monitoring methods involve physically attached sensors or non-contact vision-based vibration measurements. However, these methods have significant drawbacks due to the low spatial resolution, weight influence on the lightweight structure, and time/labor consumption. Recently, computer-vison-based deep learning methods like convolutional neural network (CNN) and fully convolutional neural network (FCN) have been applied for defect detection and localization, which address the aforementioned problems and obtain high accuracy. This paper proposes a novel hybrid deep learning architecture comprising CNN and temporal convolutional networks (CNN-TCN) for the computer vision-based defect detection task. Various beam samples, consisting of five different materials and various structural defects, were used to evaluate the proposed deep learning algorithms' performance. The proposed deep learning methods treat each pixel of the video frame like a sensor to extract valuable features for defect detection. Through empirical results, we demonstrate that this 'pixel-sensor' approach is more efficient and accurate and can achieve a better defect detection performance on different beam samples compared with the current state-of-the-art approaches, including CNN-long short-term memory (LSTM), CNN-bidirectional long short-term memory (BiLSTM), multi-scale CNN-LSTM, and CNN-gated recurrent unit(GRU) methods. [ABSTRACT FROM AUTHOR]

Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded space and lose generalizability to unseen classes. This paper proposes a noise-assisted representation learning method for fine-grained image retrieval to mitigate these issues. In the proposed work, class manifold learning is performed in which positive pairs are created with noise insertion operation instead of tightening class clusters. And other instances are treated as negatives within the same cluster. Then a loss function is defined to penalize when the distance between instances of the same class becomes too small relative to the noise pair in that class in embedded space. The proposed approach is validated on CARS-196 and CUB-200 datasets and achieved better retrieval results (85.38% recall@1 for CARS-196% and 70.13% recall@1 for CUB-200) compared to other existing methods. [ABSTRACT FROM AUTHOR]

Automatic weight detection is an essential step in the factory production of Pleurotus eryngii. In this study, a data set containing 1154 Pleurotus eryngii images was created, and then machine vision technology was used to extract eight two-dimensional features from the images. Because the fruiting bodies of Pleurotus eryngii have different shapes, these features were less correlated with weight. This paper proposed a multidimensional feature derivation method and an Attention-Based CNN model to solve this problem. This study aimed to realize the traditional feature screening task by deep learning algorithms and built an estimation model. Compared with different regression algorithms, the R2, RMSE, MAE, and MAPE of the Attention-Based CNN were 0.971, 7.77, 5.69, and 5.87%, respectively, and showed the best performance. Therefore, it can be used as an accurate, objective, and effective method for automatic weight measurements of Pleurotus eryngii. [ABSTRACT FROM AUTHOR]

Fruits classification is one of the influential applications of computer vision. Traditional classification models are trained by considering various features such as color, shape, texture, etc. These features are common for different varieties of the same fruit. Therefore, a new set of features is required to classify the fruits belonging to the same class. In this paper, we have proposed an optimized method to classify intra-class fruits using deep convolutional layers. The proposed architecture is capable of solving the challenges of a commercial tray-based system in the supermarket. As the research in intra-class classification is still in its infancy, there are challenges that have not been tackled. So, the proposed method is specifically designed to overcome the challenges related to intra-class fruits classification. The proposed method showcases an impressive performance for intra-class classification, which is achieved using a few parameters than the existing methods. The proposed model consists of Inception block, Residual connections and various other layers in very precise order. To validate its performance, the proposed method is compared with state-of-the-art models and performs best in terms of accuracy, loss, parameters, and depth. [ABSTRACT FROM AUTHOR]

Online defect detection in small industrial parts is of paramount importance for building closed loop intelligent manufacturing systems. However, high-efficiency and high-precision detection of surface defects in these manufacturing systems is a difficult task and poses a major research challenge. The small sample size of industrial parts available for training machine learning algorithms and the low accuracy of computer vision-based inspection algorithms are the bottlenecks that restrict the development of efficient online defect detection technology. To address these issues, we propose a small sample gear face defect detection method based on a Deep Convolutional Generative Adversarial Network (DCGAN) and a lightweight Convolutional Neural Network (CNN) in this paper. Initially, we perform data augmentation by using DCGAN and traditional data enhancement methods which effectively increase the size of the training data. In the next stage, we perform defect classification by using a lightweight CNN model which is based on the state-of-the-art Vgg11 network. We introduce the Leaky ReLU activation function and a dropout layer in the proposed CNN. In the experimental evaluation, the proposed framework achieves a high score of 98.40%, which is better than that of the classic Vgg11 network model. The method proposed in this paper is helpful for the detection of defects in industrial parts when the available sample size for training is small. [ABSTRACT FROM AUTHOR]

Turmeric is affected by various diseases during its growth process. Not finding its diseases at early stages may lead to a loss in production and even crop failure. The most important thing is to accurately identify diseases of the turmeric plant. Instead of using multiple steps such as image pre-processing, feature extraction, and feature classification in the conventional method, the single-phase detection model is adopted to simplify recognizing turmeric plant leaf diseases. To enhance the detection accuracy of turmeric diseases, a deep learning-based technique called the Improved YOLOV3-Tiny model is proposed. To improve detection accuracy than YOLOV3-tiny, this method uses residual network structure based on the convolutional neural network in particular layers. The results show that the detection accuracy is improved in the proposed model compared to the YOLOV3-Tiny model. It enables anyone to perform fast and accurate turmeric leaf diseases detection. In this paper, major turmeric diseases like leaf spot, leaf blotch, and rhizome rot are identified using the Improved YOLOV3-Tiny algorithm. Training and testing images are captured during both day and night and compared with various YOLO methods and Faster R-CNN with the VGG16 model. Moreover, the experimental results show that the Cycle-GAN augmentation process on turmeric leaf dataset supports much for improving detection accuracy for smaller datasets and the proposed model has an advantage of high detection accuracy and fast recognition speed compared with existing traditional models. [ABSTRACT FROM AUTHOR]

Analysis of reports published by the leading national centers for monitoring wildfires and other emergencies revealed that the devastation caused by wildfires has increased by 2.96-fold when compared to a decade earlier. The reports show that the total number of wildfires is declining; however, their impact on the wildlife appears to be more devastating. In recent years, deep neural network models have demonstrated state-of-the-art accuracy on many computer vision tasks. In this paper, we describe the design and implementation of a lightweight wildfire image classification model (LW-FIRE) based on convolutional neural networks. We explore different ways of using the existing dataset to efficiently train a deep convolutional neural network. We also propose a new method for dataset transformation to increase the number of samples in the dataset and improve the accuracy and generalization of the deep learning model. Experimental results show that the proposed model outperforms the state-of-the-art methods, and is suitable for real-time classification of wildfire images. [ABSTRACT FROM AUTHOR]

Recognition and classification for construction activities help to monitor and manage construction workers. Deep learning and computer vision technologies have addressed many limitations of traditional manual methods in complex construction environments. However, distinguishing different workers and establishing a clear recognition logic remain challenging. To address these issues, we propose a novel construction activity recognition method that integrates multiple deep learning algorithms. To complete this research, we created three datasets: 727 images for construction entities, 2546 for posture and orientation estimation, and 5455 for worker re-identification. First, a YOLO v5-based model is trained for worker posture and orientation detection. A person re-identification algorithm is then introduced to distinguish workers by tracking their coordinates, body and head orientations, and postures over time, then estimating their attention direction. Additionally, a YOLO v5-based object detection model is developed to identify ten common construction entity objects. The worker's activity is determined by combining their attentional orientation, positional information, and interaction with detected construction entities. Ten video clips are selected for testing, and a total of 745 instances of workers are detected, achieving an accuracy rate of 88.5%. With further refinement, this method shows promise for a broader application in construction activity recognition, enhancing site management efficiency. [ABSTRACT FROM AUTHOR]

The performance and serviceability of asphalt pavements have a direct influence on people's daily lives. Timely detection of pavement cracks is crucial in the task of periodic pavement survey. This paper proposes and verifies a novel computer vision-based method for recognizing pavement crack patterns. Image processing techniques, including Gaussian steerable filters, projection integrals, and image texture analyses, are employed to characterize the surface condition of asphalt pavement roads. Light Gradient Boosting Machine, Deep Neural Network, and Convolutional Neural Network are employed to recognize various patterns including longitudinal, transverse, diagonal, minor fatigue, and severe fatigue cracks. A dataset, including 12,000 samples, has been collected to construct and verify the computer vision-based approaches. Based on experiments, it can be found that all three machine learning models are capable of delivering good categorization results with an accuracy rate > 0.93 and Cohen's Kappa coefficient > 0.76. Notably, the Light Gradient Boosting Machine has achieved the most desired performance with an accuracy rate > 0.96 and Cohen's Kappa coefficient > 0.88. [ABSTRACT FROM AUTHOR]

Ancient numismatics, the study of ancient coins, has in recent years become an attractive domain for the application of computer vision and machine learning. Though rich in research problems, the predominant focus in this area to date has been on the task of attributing a coin from an image, that is of identifying its issue. This may be considered the cardinal problem in the field and it continues to challenge automatic methods. In the present paper, we address a number of limitations of previous work. Firstly, the existing methods approach the problem as a classification task. As such, they are unable to deal with classes with no or few exemplars (which would be most, given over 50,000 issues of Roman Imperial coins alone), and require retraining when exemplars of a new class become available. Hence, rather than seeking to learn a representation that distinguishes a particular class from all the others, herein we seek a representation that is overall best at distinguishing classes from one another, thus relinquishing the demand for exemplars of any specific class. This leads to our adoption of the paradigm of pairwise coin matching by issue, rather than the usual classification paradigm, and the specific solution we propose in the form of a Siamese neural network. Furthermore, while adopting deep learning, motivated by its successes in the field and its unchallenged superiority over classical computer vision approaches, we also seek to leverage the advantages that transformers have over the previously employed convolutional neural networks, and in particular their non-local attention mechanisms, which ought to be particularly useful in ancient coin analysis by associating semantically but not visually related distal elements of a coin's design. Evaluated on a large data corpus of 14,820 images and 7605 issues, using transfer learning and only a small training set of 542 images of 24 issues, our Double Siamese ViT model is shown to surpass the state of the art by a large margin, achieving an overall accuracy of 81%. Moreover, our further investigation of the results shows that the majority of the method's errors are unrelated to the intrinsic aspects of the algorithm itself, but are rather a consequence of unclean data, which is a problem that can be easily addressed in practice by simple pre-processing and quality checking. [ABSTRACT FROM AUTHOR]

Accurate handwriting recognition has been a challenging computer vision problem, because static feature analysis of the text pictures is often inadequate to account for high variance in handwriting styles across people and poor image quality of the handwritten text. Recently, by introducing machine learning, especially convolutional neural networks (CNNs), the recognition accuracy of various handwriting patterns is steadily improved. In this paper, a deep CNN model is developed to further improve the recognition rate of the MNIST handwritten digit dataset with a fast-converging rate in training. The proposed model comes with a multi-layer deep arrange structure, including 3 convolution and activation layers for feature extraction and 2 fully connected layers (i.e., dense layers) for classification. The model's hyperparameters, such as the batch sizes, kernel sizes, batch normalization, activation function, and learning rate are optimized to enhance the recognition performance. The average classification accuracy of the proposed methodology is found to reach 99.82% on the training dataset and 99.40% on the testing dataset, making it a nearly error-free system for MNIST recognition. [ABSTRACT FROM AUTHOR]

In computer vision, the convolutional neural network (CNN) is a very popular model used for emotion recognition. It has been successfully applied to detect various objects in digital images with remarkable accuracy. In this paper, we extracted learned features from a pre-trained CNN and evaluated different machine learning (ML) algorithms to perform classification. Our research looks at the impact of replacing the standard SoftMax classifier with other ML algorithms by applying them to the FC6, FC7, and FC8 layers of Deep Convolutional Neural Networks (DCNNs). Experiments were conducted on two well-known CNN architectures, AlexNet and VGG-16, using a dataset of masked facial expressions (MLF-W-FER dataset). The results of our experiments demonstrate that Support Vector Machine (SVM) and Ensemble classifiers outperform the SoftMax classifier on both AlexNet and VGG-16 architectures. These algorithms were able to achieve improved accuracy of between 7% and 9% on each layer, suggesting that replacing the classifier in each layer of a DCNN with SVM or ensemble classifiers can be an efficient method for enhancing image classification performance. Overall, our research demonstrates the potential for combining the strengths of CNNs and other machine learning (ML) algorithms to achieve better results in emotion recognition tasks. By extracting learned features from pre-trained CNNs and applying a variety of classifiers, we provide a framework for investigating alternative methods to improve the accuracy of image classification. [ABSTRACT FROM AUTHOR]

Deep learning is a subfield of artificial intelligence that allows the computer to adopt and learn some new rules. Deep learning algorithms can identify images, objects, observations, texts, and other structures. In recent years, scene text recognition has inspired many researchers from the computer vision community, and still, it needs improvement because of the poor performance of existing scene recognition algorithms. This research paper proposed a novel approach for scene text recognition that integrates bidirectional LSTM and deep convolution neural networks. In the proposed method, first, the contour of the image is identified and then it is fed into the CNN. CNN is used to generate the ordered sequence of the features from the contoured image. The sequence of features is now coded using the Bi-LSTM. Bi-LSTM is a handy tool for extracting the features from the sequence of words. Hence, this paper combines the two powerful mechanisms for extracting the features from the image, and contour-based input image makes the recognition process faster, which makes this technique better compared to existing methods. The results of the proposed methodology are evaluated on MSRATD 50 dataset, SVHN dataset, vehicle number plate dataset, SVT dataset, and random datasets, and the accuracy is 95.22%, 92.25%, 96.69%, 94.58%, and 98.12%, respectively. According to quantitative and qualitative analysis, this approach is more promising in terms of accuracy and precision rate. [ABSTRACT FROM AUTHOR]

Researchers in computer vision and machine learning are becoming increasingly interested in image semantic segmentation. Many methods based on convolutional neural networks (CNNs) have been proposed and have made considerable progress in the building extraction mission. This other methods can result in suboptimal segmentation outcomes. Recently, to extract buildings with a great precision, we propose a model which can recognize all the buildings and present them in mask with white and the other classes in black. This developed network, which is based on U-Net, will boost the model's sensitivity. This paper provides a deep learning approach for building detection on satellite imagery applied in Casablanca city, Firstly, to begin we describe the terminology of this field. Next, the main datasets exposed in this project which's 1000 satellite imagery. Then, we train the model UNET for 25 epochs on the training and validation datasets and testing the pretrained weight model with some unseen satellite images. Finally, the experimental results show that the proposed model offers good performance obtained as a binary mask that extract all the buildings in the region of Casablanca with a higher accuracy and entirety to achieve an average F1 score on test data of 0.91. [ABSTRACT FROM AUTHOR]

Accurate selection of embryos with the maximum implementation condition is a necessary step to increase the effectiveness of fertility treatment in in vitro fertilization (IVF). The deep learning algorithms presented high potential for monitoring and visualizing embryo features such as cell numbers and their morphological development in time series manner. Due to the ability of the computer vision and deep learning algorithms, this paper aimed to present a novel deep learning approach to distinguish simultaneous abnormality of embryos in time-lapse systems for detecting live and non-live births in IVF. The approach is composed of local binary convolutional neural network (LBCNN) and long short-term memory (LSTM). The LBCNN improved accuracy of classification by employing deep and local feature sets with lower number of learnable parameters in comparison with a standard convolutional layer. Moreover, LSTM network is employed to analyze temporal information of time-lapse embryos. The results indicate that the proposed approach achieves significant results in ROC analysis (0.98) in 5 days of monitoring compared to state-of-the-art models. In addition, the approach showed compatible results in early diagnosis of abnormality detection (72 hours) with 82.8% accuracy of classification compared to the pretrained well-known convolutional neural network (CNN) models and baseline CNN. [ABSTRACT FROM AUTHOR]

In computer vision, fine-grained classification has become an important issue in recognizing objects with slight visual differences. Usually, it is challenging to generate good performance when solving fine-grained classification problems using traditional convolutional neural networks. To improve the accuracy and training time of convolutional neural networks in solving fine-grained classification problems, this paper proposes a tree-structured framework by eliminating the effect of differences between clusters. The contributions of the proposed method include the following three aspects: (1) a self-supervised method that automatically creates a classification tree, eliminating the need for manual labeling; (2) a machine-learning matcher which determines the cluster to which an item belongs, minimizing the impact of inter-cluster variations on classification; and (3) a pruning criterion which filters the tree-structured classifier, retaining only the models with superior classification performance. The experimental evaluation of the proposed tree-structured framework demonstrates its effectiveness in reducing training time and improving the accuracy of fine-grained classification across various datasets in comparison with conventional convolutional neural network models. Specifically, for the CUB 200 2011, FGVC aircraft, and Stanford car datasets, the proposed method achieves a reduction in training time of 32.91%, 35.87%, and 14.48%, and improves the accuracy of fine-grained classification by 1.17%, 2.01%, and 0.59%, respectively. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN), as a classic deep learning algorithm, has been applied to various computer vision tasks. However, most classic CNN models focus on the extraction and utilisation of spatial domain features, while ignoring the potential ability of frequency domain feature extraction. In this study, the mechanism in the backbone design is explored. Firstly, the traditional DCT formula is converted into a convolution form through mathematical derivation. On the basis of a a new type of convolution, namely the DCT Convolution is designed. It is more applicable to deep learning network architectures. Secondly, based on the DCT Convolution, a new cross‐domain fusion network named CDF‐Net is designed. The frequency domain and spatial domain features of the input sample are extracted and fused by the network. CDF‐Net is a general network framework which can be applied to most existing prevalent networks. Finally, various experiments are conducted. On image classification task, for Imagenet2012 dataset, the method proposed was applied to ResNet50, and the accuracy of Top1 was increased by 3.684%. On object detection task, for COCO2017 dataset, the method proposed in this study was applied to ResNet50 and ResNeXt50, mAP were improved by 0.5% and 1.2% respectively. [ABSTRACT FROM AUTHOR]