Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring by Yuchen Gao, Qing Yang, Shiyu Zhang, Dexin Gao

“…Therefore, a target detection model based on the improved YOLOv5 (You Only Look Once) algorithm is proposed for the features generated by lithium battery combustion, using the K-means algorithm to cluster and analyse the target locations within the dataset, while adjusting the residual structure and the number of convolutional kernels in the network and embedding a convolutional block attention module (CBAM) to improve the detection accuracy without affecting the detection speed. …”
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A Noise-Robust Deep-Learning Framework for Weld-Defect Detection in Magnetic Flux Leakage Systems by Junlin Yang, Senxiang Lu

“…We also devise an up–down sampling denoising block to better filter the noise component and generate a noise-invariant representation for weld-defect detection. …”
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An abnormal traffic detection method for chain information management system network based on convolutional neural network by Chao Liu, Chunxiang Liu, Changrong Liu

“…CBAM improves the detection accuracy of abnormal traffic in chain information management system by adaptively adjusting channel attention. …”
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Novel Deepfake Image Detection with PV-ISM: Patch-Based Vision Transformer for Identifying Synthetic Media by Orkun Çınar, Yunus Doğan

“…PV-ISM incorporates patch extraction, positional encoding, and multiple transformer blocks with attention mechanisms to identify subtle artifacts in synthetic images. …”
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Campus risk detection using the S-YOLOv10-SIC network and a self-calibrated illumination algorithm by Qiang Zhao, Sha Liu, Shihao Zhang, Baijuan Wang

“…Abstract In order to realize intelligent and accurate campus risk detection, this paper proposes an improved YOLOv10 algorithm that integrates self-calibrated illumination algorithm. …”
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Segmentation detection method in tree-shaded environment for road cracks collected by inspection vehicle on WFU-Unet by Qiong Zhang, Shilin Huang, Haomiao Wang, Zhonghang Ji, Shuang Zheng, Yunqing Liu

“…Abstract Road cracks pose a significant safety hazard to transportation, making timely detection crucial for traffic safety. Traditional crack segmentation methods face three main issues: (1) Tree shadow background affects crack recognition in real-world environments. (2) Conventional convolutional neural networks fail to detect complete cracks. (3) Direct deconvolution during upsampling results in unclear crack details. …”
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A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng, Xiaoyan Chen

“…However, accurately and efficiently detecting citrus ripeness in complex orchard environments for selective robotic harvesting remains a challenge. …”
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Hyperspectral Imaging Combined with a Dual-Channel Feature Fusion Model for Hierarchical Detection of Rice Blast by Yuan Qi, Tan Liu, Songlin Guo, Peiyan Wu, Jun Ma, Qingyun Yuan, Weixiang Yao, Tongyu Xu

“…It also offers a valuable reference for the detection of other crop diseases.…”
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Stroke Detection in Brain CT Images Using Convolutional Neural Networks: Model Development, Optimization and Interpretability by Hassan Abdi, Mian Usman Sattar, Raza Hasan, Vishal Dattana, Salman Mahmood

“…Stroke detection using medical imaging plays a crucial role in early diagnosis and treatment planning. …”
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A Lightweight Remote-Sensing Image-Change Detection Algorithm Based on Asymmetric Convolution and Attention Coupling by Enze Zhang, Yan Li, Haifeng Lin, Min Xia

“…Therefore, to address the need for lightweight solutions in scenarios with limited computing resources, this paper proposes an attention-based lightweight remote sensing change detection network (ABLRCNet), which achieves a balance between computational efficiency and detection accuracy by using lightweight residual convolution blocks (LRCBs), multi-scale spatial-attention modules (MSAMs) and feature-difference enhancement modules (FDEMs). …”
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Automatic Detection and Classification of Dental Anomalies and Tooth Types Using Transformer-Based Yolo With GA Optimization by Sanabam Bineshwor Singh, Anuradha Laishram, Khelchandra Thongam, Khumanthem Manglem Singh

“…Dental pathology detection and tooth type categorization are crucial yet often error-prone and time-consuming tasks in dental diagnostics. …”
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MSDP-Net: A YOLOv5-Based Safflower Corolla Object Detection and Spatial Positioning Network by Hui Guo, Haiyang Chen, Tianlun Wu

“…This approach is designed to overcome issues related to the small size of safflower corollas and their tendency to be occluded in complex agricultural environments. For object detection, we introduce an improved YOLO v5m model, referred to as C-YOLO v5m, which integrates a Convolutional Block Attention Module (CBAM) into both the backbone and neck networks. …”
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Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n by Lili Song, Haixin Deng, Jianfeng Han, Xiongwei Gao

“…The proposed algorithm introduces several key enhancements: (1) an enhanced HorBlock module to facilitate multi-gradient and multi-scale superposition, thereby intensifying critical floating object characteristics; (2) an optimized CBAM attention mechanism to mitigate background noise interference and substantially elevate detection accuracy; (3) the incorporation of a minor target recognition layer to augment the model’s capacity to discern floating objects of differing dimensions across various environments; and (4) the implementation of the WIoU loss function to enhance the model’s convergence rate and regression accuracy. …”
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Serum glutamic oxaloacetic transaminase: a versatile cardiac biomarker for early detection of acute myocardial infarction by Srajana Shetty, Rakshita Paladi, Sachin Basavaraj, Jayasheela Hiremath, Sanatkumar B. Nyamagoud, Agadi Hiremath Vishwanath Swamy, Veeresh Hubballi

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Lights-Transformer: An Efficient Transformer-Based Landslide Detection Model for High-Resolution Remote Sensing Images by Xu Wu, Xuqing Ren, Donghao Zhai, Xiangpeng Wang, Mehreen Tarif

“…By introducing a Fusion Block for enhanced multi-angle feature fusion and a Light Segmentation Head to boost inference speed, Lights-Transformer extracts detailed feature maps from high-resolution remote sensing images, enabling the accurate identification of landslide regions and significantly improving detection accuracy. …”
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LS-MambaNet: Integrating Large Strip Convolution and Mamba Network for Remote Sensing Object Detection by Lingyu Yan, Zijian He, Zhiqi Zhang, Guangqi Xie

“…Target detection plays a crucial role in the intelligent interpretation of remote sensing images and has a wide range of potential applications. …”
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An improved XAI-based DenseNet model for breast cancer detection using reconstruction and fine-tuning by Md. Alamin Talukder

“…These techniques help highlight the most relevant regions contributing to cancer detection, offering valuable insights for pathologists and clinicians. …”
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Does Low-Field MRI Tenography Improve the Detection of Naturally Occurring Manica Flexoria Tears in Horses? by Anton D. Aßmann, José Suàrez Sànchez-Andrade, David Argüelles, Andrea S. Bischofberger

“…Standing low-field MRI tenography (MRIt) may improve the detection rate of MF tears. This study aimed to compare ultrasonography, contrast radiography, pre-contrast MRI, and MRIt to detect naturally occurring MF lesions in horses undergoing tenoscopy. …”
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Across-Beam Signal Integration Approach with Ubiquitous Digital Array Radar for High-Speed Target Detection by Le Wang, Haihong Tao, Aodi Yang, Fusen Yang, Xiaoyu Xu, Huihui Ma, Jia Su

“…This work extends the LTCI technique to the beam domain, offering a robust framework for high-speed weak target detection.…”
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Enhanced ROI guided deep learning model for Alzheimer’s detection using 3D MRI images by Israt Jahan Khan, Md. Fahim Bin Amin, Md. Delwar Shahadat Deepu, Hazera Khatun Hira, Asif Mahmud, Anas Mashad Chowdhury, Salekul Islam, Md. Saddam Hossain Mukta, Swakkhar Shatabda

“…In this paper, we propose a Regions of Interest (ROI)-guided detection paradigm to address these challenges. We employ a 3D ResNet integrated with a Convolutional Block Attention Module (CBAM), demonstrating that emphasising ROIs in brain imaging can substantially reduce both computational expenditure and training time. …”
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