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    DP-YOLO: A Lightweight Real-Time Detection Algorithm for Rail Fastener Defects by Lihua Chen, Qi Sun, Ziyang Han, Fengwen Zhai

    Published 2025-03-01
    “…To enable accurate and efficient real-time detection of rail fastener defects under resource-constrained environments, we propose DP-YOLO, an advanced lightweight algorithm based on YOLOv5s with four key optimizations. …”
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  4. 524

    Leveraging large language models for automated detection of velopharyngeal dysfunction in patients with cleft palate by Myranda Uselton Shirk, Catherine Dang, Jaewoo Cho, Hanlin Chen, Lily Hofstetter, Jack Bijur, Claiborne Lucas, Andrew James, Ricardo-Torres Guzman, Andrea Hiller, Noah Alter, Amy Stone, Maria Powell, Matthew E. Pontell, Matthew E. Pontell

    Published 2025-03-01
    “…BackgroundHypernasality, a hallmark of velopharyngeal insufficiency (VPI), is a speech disorder with significant psychosocial and functional implications. Conventional diagnostic methods rely heavily on specialized expertise and equipment, posing challenges in resource-limited settings. …”
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  5. 525

    Employing SAE-GRU deep learning for scalable botnet detection in smart city infrastructure by Usman Tariq, Tariq Ahamed Ahanger

    Published 2025-04-01
    “…These findings enhance the understanding of IoT security by offering a scalable and resource-efficient solution for botnet detection. The functional investigation establishes a foundation for future research into adaptive security mechanisms that address emerging threats and highlights the practical potential of advanced deep learning techniques in safeguarding next-generation smart city ecosystems.…”
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  6. 526
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    A lightweight algorithm for steel surface defect detection using improved YOLOv8 by Shuangbao Ma, Xin Zhao, Li Wan, Yapeng Zhang, Hongliang Gao

    Published 2025-03-01
    “…Finally, the SIoU (Simplified IoU ) is used to replace the traditional CIoU loss function, which can make the anchor frame more fast and accurate in the regression process, to improve the stability and the robustness of detection. …”
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    Article
  8. 528

    SurfaceVision: An Automated Module for Surface Fault Detection in 3D Printed Products by Laukesh Kumar, Manoj Kumar Satyarthi

    Published 2025-01-01
    “…The traditional method currently requires visual information processing devices or continuous monitoring of the process via a camera, which is very resource consuming and costly. Machine learning techniques being used for automatic detection of the faults suffer in real time conditions with inefficient fault detection due to the inability of adaptation to real time changes in the printing process. …”
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    Lightweight and Accurate YOLOv7-Based Ensembles With Knowledge Distillation for Urinary Sediment Detection by Keita Sasaki, Hiroki Nishikawa, Ittetsu Taniguchi, Takao Onoye

    Published 2025-01-01
    “…Urine sediment analysis plays an important role in evaluating kidney function. In addition to improving detection accuracy, reducing model size is also a key challenge, especially when considering deployment on medical devices where computational resources are limited. …”
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  12. 532

    GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm by Xiangqiang Kong, Guangmin Liu, Yanchen Gao

    Published 2025-05-01
    “…Simultaneously, the model size is reduced by 25.4%, the parameter count is cut down by 28.6%, and the computational resource consumption is reduced by 26.8%. This successfully achieves the harmonization of detection precision and model lightweighting.…”
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  13. 533

    Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny by Yuyang Miao, Xihan Wang, Ning Zhang, Kai Wang, Lianhe Shao, Quanli Gao

    Published 2024-12-01
    “…The algorithm’s performance in handling object occlusion and multi-scale detection is enhanced by introducing the VarifocalLoss loss function and improving the feature fusion network to BiFPN. …”
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  14. 534

    RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images by Yansong Wang, Xuanxi Yang, Haoyu Wang, Huihua Wang, Zaiqing Chen, Lijun Yun

    Published 2025-04-01
    “…Furthermore, to optimize the detection performance under hardware resource constraints, we apply knowledge distillation to RSWD-YOLO, thereby further improving the detection accuracy. …”
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  15. 535

    Automatic Detection and Identification of Underdense Meteors Based on YOLOv8n-BP Model by Siyuan Chen, Guobin Yang, Chunhua Jiang, Tongxin Liu, Xuhui Liu

    Published 2025-04-01
    “…Utilizing the Fresnel oscillation properties of meteor echoes, a BP network based on a Gaussian activation function is designed in this paper to enable it to detect meteor head and tail positions more accurately. …”
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    Near real-time monitoring of carbon effects from continuous forest change in rapidly urbanizing region of China from 2000 to 2020 by Dou Zhang, Xiaojing Tang, Shuaizhi Lu, Xiaolei Geng, Zhaowu Yu, Yujing Xie, Si Peng, Xiangrong Wang

    Published 2025-08-01
    “…The asymmetric carbon effect suggests that forest cover change may act as a carbon source even with net-zero or net-positive forest cover change. …”
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    YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

    Published 2025-07-01
    “…Unmanned Aerial Vehicles (UAVs) face a significant challenge in balancing high accuracy and high efficiency when performing real-time object detection tasks, especially amidst intricate backgrounds, diverse target scales, and stringent onboard computational resource constraints. …”
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  20. 540

    Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization by Haifei Xia, Haiyan Zhou, Mingao Zhang, Qingyi Zhang, Chenlong Fan, Yutu Yang, Shuang Xi, Ying Liu

    Published 2025-04-01
    “…The method integrates the variable action space and the composite reward function and achieves the balanced optimization of different types of defect detection performance by adjusting the scaling and translation amplitude of the detection region. …”
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