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Due to the emergence and advancement of deep learning technologies, scene text detection is becoming more widespread in various fields. However, due to the complexity of distances, angles and backgrounds, the adjacent texts in images have the problem that the detection boxes are far away from the texts, i.e., a position is not accurate enough. In this paper, we propose a text detection method centered on double ResNet-based and changed channels recursive feature pyramid, which integrates ResNet50-Mish and Res2Net50-Mish, as well as using recursive feature pyramid with changed channels. Firstly, scene images are fed into ResNet50-Mish and Res2Net50-Mish of double ResNet-based, and results are passed through a weight-based addition step to generate the fused feature maps. Secondly, the processed feature maps of double ResNet-based are sent into changed channels recursive feature pyramid to obtain feature maps with enhanced feature information. Also, the relevant segmentation results are then obtained by concatenating and convoluting. Finally, the results are given to progressive scale expansion algorithm to output the location of texts in images. The proposed model is trained and tested on ICDAR15 and CTW1500 benchmark datasets. In terms of precision values, our method outperforms or is comparable to state-of-the-art methods. In particular, experimental results achieve 91.53% precision on ICDAR15 dataset and 84.89% precision on CTW-1500 dataset. [ABSTRACT FROM AUTHOR]

In recent years, deep metric learning and its probabilistic extensions claimed state-of-the-art results in the face verification task. Despite improvements in face verification, probabilistic methods received little attention in the research community and practical applications. Previous metric learning benchmarks avoided comparison with probabilistic methods, and it is still unclear whether they generalize well to image retrieval tasks beyond faces. In this paper, we, for the first time, perform an in-depth analysis and unified comparison of known probabilistic methods in verification and retrieval tasks. We study different design choices and argue that many of them have limited impact, while only a few probabilistic methods surpass modern metric learning approaches. Combining the best ideas of earlier works, we propose a simple modification of the top-performing method, achieving new state-of-the-art results among probabilistic methods. Finally, we study confidence prediction and show that it correlates with data quality, but contains little information about prediction error probability. We thus provide a new confidence evaluation benchmark and establish a baseline for future confidence prediction research. PyTorch implementation is publicly released. [ABSTRACT FROM AUTHOR]

Accurately localizing bare-hands is crucial for human–computer interaction systems. In real-time systems, computational time plays a pivotal role in achieving this task within a specified timeframe. The localization process may involve detection, tracking, or both, depending on the framework's needs. Most studies on bare-hand detection and tracking have been conducted in controlled environments. However, localizing bare-hands in uncontrolled environments are challenging due to the complexity of variations, such as changes in illumination, rotation, occlusion, scale, pose, speed, and impostor bare-hands. These factors can significantly impact the performance of the models, along with background feature domination effect and motion blur, which further complicate localization. To address these challenges, this paper presents a comprehensive analysis of the most significant deep learning-based hand localization models. We have customized an object detector as a bare-hand localization model by incorporating detection and tracking modules, providing computationally efficient performance while addressing the variations. To evaluate the models' efficiency across a range of scales, we have proposed a scale-based bare-hand database with varying scales from 20 to 200 cm. We have also evaluated these models on various bare-hand benchmark databases. [ABSTRACT FROM AUTHOR]

With the gradual growth of deep learning in machine vision, efficient extraction of 3D point clouds becomes significant. The raw data of the 3D point cloud are sparse, disordered, and immersed in noise, which makes it difficult to classify and segment. Whether 3D point clouds can be classified and segmented or not, the local feature is an essential ingredient. Therefore, this paper proposes a GateNet-based PointNet++ network (G-PointNet++). G-PointNet++ extracts local features more accurately than PointNet++ by suppressing irrelevant features and emphasizing important features. Meanwhile, it refines the feature adaptively. Besides, the SENet and attention mechanism are introduced into PointNet++. G-PointNet++ was evaluated on the public ModelNet dataset, ShapeNet dataset, and S3DIS dataset, and its effectiveness in classification and segmentation tasks was verified. In the classification task, G-PointNet++ achieves an overall classification accuracy (OA) of 95.5% on ModelNet10 and 93.3% on ModelNet40. In the segmentation task, the mIoU of G-PointNet++ reaches 85.5% on ShapeNet. These experiments show that G-PointNet++ achieves better performance and saves more time than PointNet++, and its overall accuracy is higher than that of PCT network on ModeNet40. [ABSTRACT FROM AUTHOR]

Rice which is a staple food crop in most Asian countries mainly suffers from higher yield loss due to different factors, and one of the common factors that affect rice yield is rice leaf disease. Many countries do not allow the plantation of new rice varieties unless they are disease-resistant. Hence, disease control serves as an important factor for rice production and this can be effectively accomplished by identifying the rice disease at an early stage which is required for timely pesticide application and disease control. The rice leaf diseases are mainly inspected by the farmers with their naked eye which is error-prone, time-consuming, and often leads to huge yield loss if predicted wrong. This process is effectively handled by different researchers via the use of computer vision and machine learning techniques. To overcome the drawbacks associated with manual processing and low recognition accuracy of different statistical and machine learning techniques, this paper presents a novel concept that uses 3D 2D CNN for feature extraction and an improved backtracking search (IBS) algorithm-optimized deep generative adversarial network (DGAN) for classification. The 3D2D deep convolutional neural network (DCNN) is formed by integrating the 3D fast-learning block with a 2DCNN for extracting the rice disease features such as lesions and shape. The IBS algorithm-optimized GAN architecture is used for rice disease classification and the IBS algorithm is mainly adopted to overcome the instability and overfitting issues that arise in the DGAN. The proposed IBS-optimized GAN architecture offers improved accuracy of 98.7% which is relatively higher than the existing techniques such as extreme gradient boosting (XGBoost), transfer learning techniques, and support vector machine (SVM). [ABSTRACT FROM AUTHOR]

Recently, the world has witnessed a great increase in drone applications and missions. Drones must be detected quickly, effectively, and precisely when they are being handled illegally. Vision-based anti-drone systems provide an efficient performance compared to radar- and acoustic-based systems. The effectiveness of drone detection is affected by a number of issues, including the drone’s small size, conflicts with other objects, and noisy backgrounds. This paper employs enlarging the effective receptive field (ERF) of feature maps generated from the YOLOv6 backbone. First, RepLKNet is used as the backbone of YOLOv6, which deploys large kernels with depth-wise convolution. Then, to get beyond RepLKNet’s large inference time, a novel LERFNet is implemented. LERFNet uses dilated convolution in addition to large kernels to enlarge the ERF and overcome each other’s problems. The linear spatial-channel attention module (LAM) is used to give more attention to the most informative pixels and high feature channels. LERFNet produces output feature maps with a large ERF and high shape bias to enhance the detection of various drone sizes in complex scenes. The RepLKNet and LERFNet backbones for Tiny-YOLOv6, Tiny-YOLOv6, YOLOv5s, and Tiny-YOLOv7 are compared. In comparison to the aforementioned techniques, the suggested model’s results show a greater balance between accuracy and speed. LERFNet increases the MAP by 2.8%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$2.8\%$$\end{document}, while significantly reducing the GFLOPs and parameter numbers when compared to the original backbone of YOLOv6. [ABSTRACT FROM AUTHOR]