Search

Deep neural networks have achieved outstanding performance in computer vision tasks. Convolutional Neural Networks (CNNs) typically operate in the spatial domain with raw images, but in practice, images are usually stored and transmitted in their compressed representation where JPEG is one of the most widely used encoder. Also, these networks are computationally intensive and slow. This paper proposes performing the learning and inference processes in the compressed domain in order to reduce the computational complexity and improve the speed of popular CNNs. For this purpose, a novel graph-based frequency channel selection method is proposed to identify and select the most important frequency channels. The computational complexity is reduced by retaining the important frequency components and discarding the insignificant ones as well as eliminating the unnecessary layers of the network. Experimental results show that the modified ResNet-50 operating in the compressed domain is up to 70% faster than the spatial-based traditional ResNet-50 while resulting in similar classification accuracy. Moreover, this paper proposes a preprocessing step with partial encoding to improve the resilience to distortions caused by low-quality encoded images. Finally, we show that training a network with highly compressed data can achieve a good classification accuracy with up to 93% reduction in the storage requirements of the training data. [ABSTRACT FROM AUTHOR]

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserve the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localisation, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring. • Marine environments and fish habitats are widely surveyed using underwater cameras. • Deep learning (DL) offers an effective solution for processing underwater videos. • We write a tutorial on the key concepts and steps of DL for underwater monitoring. • We provide a survey of key DL-based visual fish monitoring methods/datasets. • We shed light on challenges/opportunities of DL for underwater fish monitoring. [ABSTRACT FROM AUTHOR]

Fine-grained visual categorization is an important task in computer vision. Prior works on fine-grained visual categorization have paid much attention to addressing intra-class variation and inter-class similarity. However, they rarely study that task from the perspective of probability distribution. In this paper, we propose a novel refined probability distribution module based on deep convolutional neural network. Our module computes the probability of an image by fully utilizing the similarity information between images. Firstly, we use deep neural networks to obtain the initial probability distribution and extract features. Then, we build a network whose inputs are features for calculating image-to-image similarity scores. Finally, our module refines the initial probability distribution based on an effective batch random walk operation with similarity scores. Our module can be plugged into many deep convolutional neural networks. Experimental results show that our approach outperforms state-of-the-art methods on the CUB-200–2011, FGVC-Aircraft and Stanford Cars datasets respectively. [ABSTRACT FROM AUTHOR]

We address the pixel-wise mapping problem that commonly exists in the fine-grained computer vision tasks, such as estimating keypoint heatmaps and segmentation masks. These tasks require, at low computation overheads, modeling the long-range dependencies among high-resolution inputs and estimating the highly nonlinear pixel-wise outputs. While the attention mechanism added to Deep Convolutional Neural Networks (DCNNs) can boost long-range dependencies, the element-specific attention, such as the Nonlocal block, is highly complex and noise-sensitive to learn, and most of the simplified attention blocks are designed for image-wise classification purposes and simply applied to pixel-wise tasks. In this paper, we present the Polarized Self-Attention (PSA) block targeting the high-quality pixel-wise mapping with: (1) Polarized filtering: keeping high internal resolution in both channel and spatial attention computation while completely collapsing input tensors along their counterpart dimensions. (2) Enhancement: composing non-linearity that directly fits the output distribution of typical pixel-wise mappings, such as the 2D Gaussian distribution (keypoint heatmaps), or the 2D Binormial distribution (binary segmentation masks). Experimental results show that PSA boosts standard baselines by 2–4 points, and boosts state-of-the-arts by 1–2 points on 2D pose estimation and semantic segmentation benchmarks. Codes are available at (https://github.com/DeLightCMU/PSA). [ABSTRACT FROM AUTHOR]

Segmentation is an important task in a wide range of computer vision applications, including medical image analysis. Recent years have seen an increase in the complexity of medical image segmentation approaches based on sophisticated convolutional neural network architectures. This progress has led to incremental enhancements in performance on widely recognised benchmark datasets. However, most of the existing approaches are computationally demanding, which limits their practical applicability. This paper presents an expand-squeeze dual multiscale residual network (ESDMR-Net), which is a full y convolutional network that is particularly well-suited for resource-constrained computing hardware such as mobile devices. ESDMR-Net focusses on extracting multiscale features, enabling the learning of contextual dependencies among semantically distinct features. The ESDMR-Net architecture allows dual-stream information flow within encoder–decoder pairs. The expansion operation (depthwise separable convolution) makes all of the rich features with multiscale information available to the squeeze operation (bottleneck layer), which then extracts the necessary information for the segmentation task. The Expand-Squeeze (ES) block helps the network pay more attention to under-represented classes, which contributes to improved segmentation accuracy. To enhance the flow of information across multiple resolutions or scales, we integrated dual multiscale residual (DMR) blocks into the skip connection. This integration enables the decoder to access features from various levels of abstraction, ultimately resulting in more comprehensive feature representations. We present experiments on seven datasets from five distinct examples of applications: segmentation of retinal vessels (2 ×), skin lesions (2 ×), digestive tract polyps, lung regions, and cells. Our model demonstrates strong performance, with an F1 score of 0.8287%, 0.8211%, 0.9034%, 0.9451%, 0.9543%, 0.9840%, and 0.8424% on the DRIVE, CHASE, ISIC2017, ISIC2016, CVC-ClinicDB, MC and MoNuSeg datasets, respectively. Remarkably, our model achieves these results despite having significantly fewer trainable parameters, with a reduction of two or even three orders of magnitude. • Multiscale feature extraction to understand contextual dependencies. • The expand-squeeze operation facilitates multiscale information extraction. • Dual-stream information flow within encoder–decoder pairs. • The use of DMR blocks in skip connections improves the information flow. • The deep structure of the network allows it to learn more features. [ABSTRACT FROM AUTHOR]

Date fruit (Phoenix dactylifera L.) is known for its diversity in terms of genotypes that differ in shape, color, texture, taste, and other physical and sensory attributes, in addition to biochemical compounds. This richness generates complex, long, and tedious work at the scale of farm as well as in handling, processing, and storage facilities to prepare standardized, and homogenous final products that deal with consumer preferences and needs. To alleviate this, an increased importance and particular trend were observed recently for the use of artificial intelligence (AI) techniques and computer vision technology since they are considered as accurate, objective, non-destructive, fast, and efficient emerging technologies. In this context, this manuscript provides comprehensive insights into key applications of AI techniques and computer vision technology, using different classification models and algorithms developed on the basis (for example) of neural network classifiers, such as convolutional and artificial neural network, on harvest and postharvest assessment of date fruit. Thus, the importance of date fruit dataset used in the AI domain was discussed with link to all accessible datasets in the field of date palm. In addition, available and relevant AI tools and approaches applied to the postharvest of date fruit from maturity distinguishing until storage through other key handling operations, sorting, grading, etc., were detailed in the five sections of this paper. In terms of conclusion, this review showed that these smart technologies can play a substantial role and offer new opportunities to improve the whole value chain of date palm sector. • Harvesting and post-harvest handling impact quality and storability of dates. • These operations are tedious and subjective leading to quantitative/qualitative losses. • Artificial intelligence (AI) techniques are emergent technologies that can reduce these losses. • AI techniques are more accurate, fast, non-destructive and objective. [ABSTRACT FROM AUTHOR]

With the development of machine vision, target trackers based on Siamese networks have demonstrated excellent performance in balancing speed and accuracy compared to traditional methods. Traditional trackers usually use only the deep features extracted from the last convolutional neural network (CNN) layer, which contain semantic information, to complete the similarity matching. In addition, a cosine window is usually used to filter the score map, which leads to the limited suppression effect of the tracker on the interference of the objects similar to the target and the poor robustness of the tracker. In this paper, a Siamese network tracker named SiamSMDFFF, which combines shallow-middle-deep feature fusion with a clustering-based adaptive rectangular window filter, is proposed. SiamSMDFFF uses the features to fuse at the feature level to obtain the complementary feature maps and then uses the score maps calculated from the complementary feature maps via correlation to fuse at the score level and obtain the final score maps. Then, the peak points in the score map are used as the initial clustering centers to complete the clustering, and the distance between the clustering center and the farthest clustering point is calculated. Finally, the distance is used to control the change in the size of the rectangular window to filter the score map in order to overcome the negative impact of the similar targets interfering in the tracking process and improve the robustness of the tracker. The experimental results demonstrate that SiamSMDFFF is significantly improved in several aspects compared to the conventional tracker. [ABSTRACT FROM AUTHOR]

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, restoration, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging. [Display omitted] • The first survey to comprehensively covers applications of Transformers in the medical imaging domain by covering more than 125 relevant papers. • Survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. • For each application, we develop a taxonomy, highlight task-specific challenges, and provide insights about solving them based on the literature reviewed. Furthermore, we provide a critical discussion of the field's current state as a whole with promising future directions. [ABSTRACT FROM AUTHOR]

[Display omitted] • We developed AICropCAM, an edge-computing enabled camera system for crop monitoring. • It integrated Raspberry Pi and Arduino for image processing and LoRa communication. • It ran a stack of four deep neural networks to characterize multiple plant/canopy parameters. • We quantified the power consumption and speed of the system for edge image-processing. • AICropCAM is a next-generation enabling technology for real-time, low-latency Ag applications. Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with >43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations. [ABSTRACT FROM AUTHOR]

The use of Machine Learning (ML) has gained significant attention in recent years for use in the development of surrogate models capable of predicting various neutronics parameters in Pressurized Water Reactors (PWRs). These approaches aim to provide fast and accurate approximations of conventional model outputs which are typically both computationally expensive and time-consuming to run. A novel supervised learning framework LatticeNet , employing Artificial Neural Networks (ANNs) and computer vision techniques, has been shown to be particularly useful in the prediction of assembly and pin-level parameters such as multiplication factor or pin powers. This framework was validated using deterministically-generated training data for a standard fuel assembly, representing best-case input conditions. This paper investigates the performance of LatticeNet as well as the tools developed alongside when given Monte Carlo-generated inputs and assemblies of varying pin dimensions, assessing the network's tolerance to training data uncertainty and atypical configurations. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNNs) have achieved great success in computer vision applications. However, due to the high requirements for computation power and memory usage, most state-of-the-art CNNs are difficult to deploy on resource-constrained mobile devices. Although many typical lightweight neural networks have been proposed in the industry, such as MobileNetV2, which reduce the amount of parameters and calculations, they still have a lot of redundancy. Furthermore, few papers consider the use of deep learning models to implement image retrieval on terminals, so we propose a new offline retrieval framework based on lightweight neural network models, called Offline Mobile Content-Based Image Retrieval (OMCBIR). In this framework, we focus on the feature extraction of the model, by introducing pointwise group convolution and channel shuffle into the bottleneck block, reconstructing the network structure, and introducing the convolutional attention module, we propose an extremely lightweight small network-Attention-based Lightweight Network (ALNet). Compared to MobileNetV2, ALNet obtains a higher mAP on each dataset in OMCBIR when the model parameters are reduced by more than 62% and the model size is reduced by more than 63%. Extensive experiments conducted on five public datasets provide a trade-off between retrieval performance and model size of different algorithms, which proves the efficiency of the proposed OMCBIR. • We propose a novel offline framework for content-based image retrieval on the mobile side, namely OMCBIR. As far as we know, this is the first time that a deep learning model is used for a completely offline mobile content-based image retrieval task. • We propose an extremely lightweight network architecture ALNet and ALBlock unit. By introducing pointwise group convolution, channel shuffle and convolution attention module into ALBlock, we not only improve retrieval accuracy, but also greatly reduce the computational cost and memory occupation. • The experimental results on five public datasets demonstrate the effectiveness of our model. Compared to MobileNetV2, ALNet obtains a higher mAP on each dataset in OMCBIR with over 62% reduction in model parameters and over 63% reduction in model size. [ABSTRACT FROM AUTHOR]

In the past few years, convolutional neural networks (CNNs) have been proven powerful in extracting image features crucial for medical image registration. However, challenging applications and recent advances in computer vision suggest that CNNs are limited in their ability to understand the spatial correspondence between features, which is at the core of image registration. The issue is further exaggerated when it comes to multi-modal image registration, where the appearances of input images can differ significantly. This paper presents a novel cross-modal attention mechanism for correlating features extracted from the multi-modal input images and mapping such correlation to image registration transformation. To efficiently train the developed network, a contrastive learning-based pre-training method is also proposed to aid the network in extracting high-level features across the input modalities for the following cross-modal attention learning. We validated the proposed method on transrectal ultrasound (TRUS) to magnetic resonance (MR) registration, a clinically important procedure that benefits prostate cancer biopsy. Our experimental results demonstrate that for MR-TRUS registration, a deep neural network embedded with the cross-modal attention block outperforms other advanced CNN-based networks with ten times its size. We also incorporated visualization techniques to improve the interpretability of our network, which helps bring insights into the deep learning based image registration methods. The source code of our work is available at https://github.com/DIAL-RPI/Attention-Reg. • A self-attention mechanism maps correspondence for multi-modal registration. • Contrastive learning-guided pretraining captures cross-modality image features. • A network equipped with both of these methods outperforms other larger networks. [ABSTRACT FROM AUTHOR]

[Display omitted] • Recent advances and future challenges and trends in flotation froth image analysis based on deep learning are reviewed. • Most advances are driven by transfer learning with publicly available neural networks pretrained on the ImageNet database. • Labelling of images poses a fundamental challenge to the application of these systems as soft sensors. • This can be ameliorated by use of methods, such as semi-supervised regression and k-shot learning. • The availability of large, curated froth image databases could serve to drive further development. Machine vision is widely used in the monitoring of froth flotation plants as a means to assist control operators on the plant. While these systems have a mature ability to analyse physical froth features, such as the colour of the froth and bubble size distributions, research has continued to focus on their use in automated control systems, which is not well established yet. This includes functionality related to the recognition of different operational regimes, as well as their use in the inferential measurement of froth grade. The last decade has seen major breakthroughs in deep learning and advances in image processing, which have also had a direct impact on flotation froth image analysis with computer vision systems. In this paper, these advances are reviewed and future trends are identified. Convolutional neural networks that are able to learn features from froth images have redefined the state-of-the-art in froth image analysis. These models rely heavily on transfer learning, with models such as GoogLeNet and MobileNet leading in the field. Emerging trends comprise a stronger focus on dynamic froth image analysis or the analysis of froth video sequences, froth-based monitoring, exploitation of froth features in advanced control and one-shot learning approaches based on froth image synthesis. Challenges are related to the labelling of images, the computational cost associated with training deep neural networks, as well as interpretation of these models. [ABSTRACT FROM AUTHOR]

Construction project is sensitive to material arrival delays, which can cause schedule delays and budget overruns. The prompt detection of construction material arrival delays is necessary to recover disrupted projects in time. This paper explores computer vision-based (CVB) mutually coupled detection and tracking of transport trucks for monitoring construction material arrival delays. Through the mutually coupled mechanism, the truck detection and tracking algorithms complement each other in reducing accumulated tracking errors, modifying false positives and false negatives in detection, and stabilizing the detected bounding boxes. The experimental results indicate integrating a deep Convolutional Neural Network (CNN), a Kanade-Lucas-Tomasi (KLT) corner feature tracker, and a hash-based occlusion handling strategy can achieve high tracking precision compared to state-of-the-art trackers in construction. The field application results indicate that the proposed method can achieve the automatic monitoring of construction material arrival delays. • A CVB truck tracker is developed to monitor construction material arrival delays. • The CVB truck tracker implies a mutually coupled detection and tracking mechanism. • Higher truck tracking precision than previous trackers in construction is achieved. • The proposed method has been justified through field applications. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have recently emerged as a promising approach for computer vision tasks with many new DCNN architectures proposed to further improve their performance. However, the significant computation workload limits the deployment of such networks on embedded devices. Research on accelerating DCNN inference usually employs field-programmable gate arrays (FPGAs) due to their programmability. However, hardware efficiency and reconfigurability do not often receive sufficient attention. This paper proposes an efficient accelerator to support multiple DCNNs and improve the hardware utilization from three perspectives. First, a bandwidth-based tiling algorithm is used to improve the data transfer efficiency for direct memory access (DMA). Second, three parallel strategies are proposed to improve the utilization of the computing units (CUs). Third, a configurable CU is designed to improve the digital signal processor (DSP) utilization. The proposed accelerator is implemented on the Xilinx ZYNQ-7 ZC706 Evaluation Board at 200 MHz. The accelerator reaches 163 Giga Operations Per Second (GOPS) and 0.36 GOPS/DSP on the VGG-16 while consuming only 448 DSPs. A 0.24 GOPS/DSP is achieved with ResNet50 and 0.27 GOPS/DSP with YOLOv2-tiny. The experimental results demonstrate that this design achieves a better trade-off between hardware resource consumption, performance, and reconfigurability over previous works. [ABSTRACT FROM AUTHOR]

• Propose a new ODCNN-CCM model for crop classification on multispectral remote sensing images. • Employ WF based image preprocessing and RetinaNet model as a feature extractor. • Apply DSO with DSAE model is applied for the crop classification process. Multispectral remote sensing images (MRSI)are widely employed to assess modifications in water bodies, land use and land cover changes, forest degradation, landscape change, and so on. Conventionally, MRSI is applied to map crops globally. MRSI based crop classification has gained considerable interest in the areas of agricultural productivity, agricultural policies, assuring food security, and recognizing sustainable agricultural development. Recently developed deep learning models can also be employed for crop classification using multispectral remote sensing images. In this aspect, this paper presents an optimal deep convolutional neural network based crop classification model (ODCNN-CCM) using multispectral remote sensing images. The presented ODCNN-CCM technique initially employs adaptive wiener filtering based image pre-processing technique. Moreover, RetinaNet model is applied to perform feature extraction process. Finally, dolphin swarm optimization (DSO) with deep stacked denoising autoencoder (DSDAE) model is applied for crop type classification. The performance validation of the proposed technique is validated using the Indian Pines Benchmark (INB), University of Pavia Benchmark (UPB), and Salinas Scene Benchmark (SSB). The proposed model achieves maximum accuracy of 97.51%, 98.33%, and 97.75% on the INB, UPB, and SSD datasets respectively. [ABSTRACT FROM AUTHOR]

In this paper, the twin-systems approach is reviewed, implemented, and competitively tested as a post-hoc explanation-by-example solution to the eXplainable Artificial Intelligence (XAI) problem. In twin-systems, an opaque artificial neural network (ANN) is explained by "twinning" it with a more interpretable case-based reasoning (CBR) system, by mapping the feature weights from the former to the latter. Extensive comparative tests are performed, over four experiments, to determine the optimal feature-weighting method for such twin-systems. Twin-systems for traditional multilayer perceptron (MLP) networks (MLP–CBR twins), convolutional neural networks (CNNs; CNN–CBR twins), and transformers for NLP (BERT–CBR twins) are examined. In addition, Feature Activation Maps (FAMs) are explored to enhance explainability by providing an additional layer of explanatory insight. The wider implications of this research on XAI is discussed, and a code library is provided to ease replicability. [ABSTRACT FROM AUTHOR]

• Classification of roughness to unmeasured surfaces is firstly proposed. • No need to artificially design any indicators. • The method used in this article is robust to the light source environment. • This method is combined with machine vision system. At present, machine vision roughness detection mostly needs to design roughness related indexes based on images, and the index design has human intervention and is heavily dependent on the light source environment. To solve this problem, the paper classifies the surface roughness based on the deep convolutional neural network method, which can realize the roughness detection without index design. The most important thing is that the detection method has good light source robustness under different light source environments. The study adopts an end-to-end image analysis method, by means of image enhancement pre-processing of a small number of source images, after multi-layer convolution and pooling operations, as well as comprehensive processing of fully connected and classification layers, the convolutional kernel can automatically extract the features of the image, and finally the surface roughness obtained by vertical disc cutter milling can be classified and predicted. In addition, based on the experimental light source environment with two different luminance of night and day, by comparing with the technically mature ResNet50 and DenseNet121 convolutional neural network models, the deep convolutional neural network Xception model not only has high roughness classification accuracy, but also has more light source environment robustness. This method makes the online measurement of visual roughness possible. [ABSTRACT FROM AUTHOR]

Visual design is associated with the use of some basic design elements and principles. Those are applied by the designers in the various disciplines for aesthetic purposes, relying on an intuitive and subjective process. Thus, numerical analysis of design visuals and disclosure of the aesthetic value embedded in them are considered as hard. However, it has become possible with emerging artificial intelligence technologies. This research aims at a neural network model, which recognizes and classifies the design principles over different domains. The domains include artwork produced since the late 20th century; professional photos; and facade pictures of contemporary buildings. The data collection and curation processes, including the production of computationally-based synthetic dataset, is genuine. The proposed model learns from the knowledge of myriads of original designs, by capturing the underlying shared patterns. It is expected to consolidate design processes by providing an aesthetic evaluation of the visual compositions with objectivity. [Display omitted] • This paper examines quantitative aspects of visual design principles (71). • A CNN based AI model is developed to detect fundamental visual features (74). • 9 classes -stem from 3 main principles: emphasis, balance and rhythm are targeted (84). • Visual data from photography, painting and architecture domains are used for ML (82). • High accuracy rates are recorded for top 3 predictions among 5 experiment settings (85). [ABSTRACT FROM AUTHOR]