Detection of Crack Sealant in the Pretreatment Process of Hot In-Place Recycling of Asphalt Pavement via Deep Learning Method by Kai Zhao, Tianzhen Liu, Xu Xia, Yongli Zhao

“…Furthermore, the DRBNCSPELAN (Dilated Reparam Block with Cross-Stage Partial and Efficient Layer Aggregation Networks) module is introduced to ensure efficient information flow, and a lightweight shared convolution (LSC) detection head is developed. …”
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Joint Spectral Information and Spatial Details for Road Extraction From Optical Remote-Sensing Images by Yuzhun Lin, Jie Rui, Fei Jin, Shuxiang Wang, Xibing Zuo, Xiao Liu

“…Currently, satellite remote-sensing image acquisition systems typically include two forms of panchromatic and multispectral images, both of which have complementary advantages in spatial and channel dimensions. …”
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A Low Complexity Algorithm for 3D-HEVC Depth Map Intra Coding Based on MAD and ResNet by Erlin Tian, Jiabao Zhang, Qiuwen Zhang

“…In contrast, for complex CUs, we propose a lightweight ResNet (Residual Neural Network) model that substitutes standard convolutions with depthwise separable convolutions (DSC) in order to decrease the number of parameters. …”
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3D Micro-Expression Recognition Based on Adaptive Dynamic Vision by Weiyi Kong, Zhisheng You, Xuebin Lv

“…Small samples and unbalanced data are the main reasons for the low recognition accuracy of current technologies. Inspired by circular convolution networks, this paper innovatively proposes an adaptive dynamic micro-expression recognition algorithm based on self-supervised learning, namely MADV-Net. …”
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Lightweight patch-level attention for efficient pig behavior detection: A novel dataset and approach by Shuo Wan, Zhongqiang Huang, Tao Han, Ying Sha

“…Pig behavior detection involves the automatic recognition and classification of pig behaviors in farm images using computer vision and deep learning techniques. Currently, mainstream approaches for pig behavior detection utilize neural networks for image analysis and recognition, however, deep neural network architectures are inherently complex and computationally intensive, with potential for further accuracy enhancement. …”
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Potato precision planter metering system based on improved YOLOv5n-ByteTrack by Cisen Xiao, Changlin Song, Junmin Li, Min Liao, Yongfan Pu, Kun Du

“…Subsequently, re-parameterized convolution (RepConv) is incorporated into the feature extraction network architecture, enhancing the model’s inference speed by leveraging the correlation between features. …”
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CGLCS-Net: Addressing Multi-Temporal and Multi-Angle Challenges in Remote Sensing Change Detection by Ke Liu, Hang Xue, Caiyi Huang, Jiaqi Huo, Guoxuan Chen

“…Currently, deep learning networks based on architectures such as CNN and Transformer have achieved significant advances in remote sensing image change detection, effectively addressing the issue of false changes due to spectral and radiometric discrepancies. …”
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DSF-YOLO for robust multiscale traffic sign detection under adverse weather conditions by Jun Li, QinWen Deng, WenXin Gao, Bing Yang, Lan Jia, Ju Zhou, HaiBo Pu

“…This model employs an attention-based dynamic sequence fusion feature pyramid, which enhances recognition accuracy for small-target traffic sign instances in adverse weather, as opposed to traditional feature pyramid networks. Additionally, the model integrates a dynamic snake convolution operator along with Wise-IoU, enabling it to capture fine small-scale feature information while mitigating the impact of low-quality instances. …”
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Real-Time Coronary Artery Dominance Classification from Angiographic Images Using Advanced Deep Video Architectures by Hasan Ali Akyürek

“…Traditional classification methods rely on the manual visual interpretation of coronary angiograms. However, current deep learning approaches typically classify right and left coronary artery angiograms separately. …”
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Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT by Peng Shen, Keyu Mei, Haori Xue, Tenglong Li, Guoqing Zhang, Yongxiang Zhao, Wei Luo, Liang Mao

“…Pig counting is an essential activity in the administration of pig farming. Currently, manual counting is inefficient, costly, and unsuitable for systematic analysis. …”
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Utilize Data Augmentation and Flexi Corner Block for Road Damage Detection by Zhaohui Wu, Runjing Zhao, Xingliang Sun, Zhaojia Li

“…Road damage detection is crucial for ensuring road safety and maintaining infrastructure durability, especially as increasing traffic and aging road networks create growing challenges for transportation systems worldwide. …”
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Implementation for Lightweight Deep Learning for Anomaly Detection and Denoising on Gravitational Waves by R. K. Mohith Niranjen, C. Yogesh, Anirudh Vinodh, Tharun Sureshkumar, S. Vatchala

“…In addition, higher-order recurrent layers like Long Short-Term Memory(LSTM) networks are also used to precisely model temporal characteristics so that accuracy is preserved with enhanced anomaly detection and noise removal. …”
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DAMI-YOLOv8l: A multi-scale detection framework for light-trapping insect pest monitoring by Xiao Chen, Xinting Yang, Huan Hu, Tianjun Li, Zijie Zhou, Wenyong Li

“…Insect pest detection plays a crucial role in agricultural production for accurate and early pest control, thus significantly reducing crop damage and increasing yields. However, currently the small size and multi-scale characteristics of insect pests pose significant challenges for accurate object detection using computer vision technology. …”
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Application of a deep learning algorithm for the diagnosis of HCC by Philip Leung Ho Yu, Keith Wan-Hang Chiu, Jianliang Lu, Gilbert C.S. Lui, Jian Zhou, Ho-Ming Cheng, Xianhua Mao, Juan Wu, Xin-Ping Shen, King Ming Kwok, Wai Kuen Kan, Y.C. Ho, Hung Tat Chan, Peng Xiao, Lung-Yi Mak, Vivien W.M. Tsui, Cynthia Hui, Pui Mei Lam, Zijie Deng, Jiaqi Guo, Li Ni, Jinhua Huang, Sarah Yu, Chengzhi Peng, Wai Keung Li, Man-Fung Yuen, Wai-Kay Seto

“…Results: From 2,832 patients and 4,305 CT observations, the best-performing model was Spatio-Temporal 3D Convolution Network (ST3DCN), achieving area under receiver-operating-characteristic curves (AUCs) of 0.919 (95% CI, 0.903–0.935) and 0.901 (95% CI, 0.879–0.924) at the observation (n = 1,077) and patient (n = 685) levels, respectively during internal validation, compared with 0.839 (95% CI, 0.814–0.864) and 0.822 (95% CI, 0.790–0.853), respectively for standard of care radiological interpretation. …”
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A Robust Hybrid CNN+ViT Framework for Breast Cancer Classification Using Mammogram Images by Vasudha Rani Patheda, Gunda Laxmisai, B. V. Gokulnath, S. P. Siddique Ibrahim, S. Selva Kumar

“…This research addresses the variability and potential oversight in radiologists’ manual mammogram interpretations, aiming to enhance classification accuracy by combining Convolution Neural Networks (CNNs) and Vision Transformers (ViTs). …”
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Enhancing Crop Health: Advanced Machine Learning Techniques for Prediction Disease in Palm Oil Tree by Nandy Manish, Kumar Yalakala Dinesh

“…This study builds predictive models by using a palmd database comprised of the large datasets of palm oil tree health indicators, environmental factors and historical disease outbreaks to identify early signs of disease with high accuracy.To analyze both structured as well as unstructured data multiple machine learning algorithms were used such as Random Forest, Support Vector Machines, Convolution Neural Networks. Environmental variables like temperatures, humidity and soil conditions; as well as features of the leaves, including their texture and shape were given as input features to the trained models. …”
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