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    Towards Efficient SAR Ship Detection: Multi-Level Feature Fusion and Lightweight Network Design by Wei Xu, Zengyuan Guo, Pingping Huang, Weixian Tan, Zhiqi Gao

    Published 2025-07-01
    “…Thus, guided by the principles of lightweight design, robustness, and energy efficiency optimization, this study proposes a three-stage collaborative multi-level feature fusion framework to reduce model complexity without compromising detection performance. …”
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    Article
  4. 104

    Efficient wildlife monitoring: Deep learning-based detection and counting of green turtles in coastal areas by Naoya Noguchi, Hideaki Nishizawa, Taro Shimizu, Junichi Okuyama, Shohei Kobayashi, Kazuyuki Tokuda, Hideyuki Tanaka, Satomi Kondo

    Published 2025-05-01
    “…Drones have recently been used to assess wildlife populations and their abundance. The automatic detection of target animals in drone footage enables efficient abundance estimation. …”
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  5. 105

    Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 by Fangyu Hu, Mairheba Abula, Di Wang, Xuan Li, Ning Yan, Qu Xie, Xuedong Zhang

    Published 2025-07-01
    “…By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. …”
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  6. 106
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    CSCP-YOLO: A Lightweight and Efficient Algorithm for Real-Time Steel Surface Defect Detection by Chenglong Wang, Heng Wang, Yimin Jiang, Lei Yu, Xueting Wang

    Published 2025-01-01
    “…Steel surface defect detection is critical for ensuring product quality, yet remains challenging due to multi-scale defects, small target prevalence, and complex background interference. …”
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    Article
  9. 109

    YOLO-Air: An Efficient Deep Learning Network for Small Object Detection in Drone-Based Imagery by Jigang Qiu, Fangkai Cai, Ning Fu, Yuanfei Yao

    Published 2025-01-01
    “…However, it poses unique challenges for object detection due to small objects, complex backgrounds, and noise interference. …”
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  10. 110

    Lightweight patch-level attention for efficient pig behavior detection: A novel dataset and approach by Shuo Wan, Zhongqiang Huang, Tao Han, Ying Sha

    Published 2025-12-01
    “…The pig farming industry plays a crucial role in food production system. Pig behavior detection facilitates timely anomaly detection, enhances farming efficiency, ensures animal welfare, and prevents diseases. …”
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  11. 111

    REDETR-RISTD: Real-Time Long-Range Infrared Small Target Detection Network Based on the Reparameterized Efficient Detection Transformer by Ning Li, Daozhi Wei

    Published 2025-04-01
    “…This happens because the targets are small in size, have a weak signal-to-noise ratio (SNR), and are surrounded by complex backgrounds. A novel real-time long-range infrared small target detection network based on the Reparameterized Efficient Detection Transformer (REDETR-RISTD) is proposed. …”
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  12. 112

    Real-time and resource-efficient banana bunch detection and localization with YOLO-BRFB on edge devices by Shuo Wang, Shuo Wang, Shuo Wang, Lijiao Wei, Danran Zhang, Danran Zhang, Ling Chen, Ling Chen, Weihua Huang, Dongjie Du, Kangmin Lin, Zhenhui Zheng, Zhenhui Zheng, Zhenhui Zheng, Jieli Duan

    Published 2025-08-01
    “…Current methods face challenges in achieving high detection accuracy and efficient deployment due to their structural complexity and significant computational demands. …”
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    Article
  13. 113

    RiceNet: Efficient CNN for High-Throughput Image-Based Rice Panicle Detection and Counting by Kushwaha Ragini, Balkrishna Sutar Manisha

    Published 2025-01-01
    “…Traditionally, therefore, accurate and efficient detection and counting of rice panicles are labor intensive. …”
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  14. 114

    Efficient automated detection of power quality disturbances using nonsubsampled contourlet transform & PCA-SVM by Pampa Sinha, Kaushik Paul, Asit Mohanty, IM Elzein, Chandra Sekhar Mishra, Mohamed Metwally Mahmoud, Daniel Eutyche Mbadjoun Wapet, Abdulrahman Al Ayidh, Ahmed Althobaiti, Hany S Hussein, Thamer AH Alghamdi, Ahmed M Ewais

    Published 2025-05-01
    “…The increasing complexity of modern power systems, driven by smart grid technologies, dispersed generation, and renewable energy sources, has made power quality (PQ) evaluation more challenging. …”
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    Can Separation Enhance Fusion? An Efficient Framework for Target Detection in Multimodal Remote Sensing Imagery by Yong Wang, Jiexuan Jia, Rui Liu, Qiusheng Cao, Jie Feng, Danping Li, Lei Wang

    Published 2025-04-01
    “…Leveraging this framework, we present Sep-Fusion—an efficient target detection approach tailored for multimodal remote sensing aerial imagery. …”
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    EMB-YOLO: A Lightweight Object Detection Algorithm for Isolation Switch State Detection by Haojie Chen, Lumei Su, Riben Shu, Tianyou Li, Fan Yin

    Published 2024-10-01
    “…This module is designed with a lightweight structure, aimed at reducing the computational complexity and parameter count, thereby optimizing the model’s computational efficiency. …”
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  19. 119

    Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring by Sicheng You, Boheng Li, Yijia Chen, Zhiyan Ren, Yongying Liu, Qingyang Wu, Jianghan Tao, Zhijie Zhang, Chenyu Zhang, Feng Xue, Yulun Chen, Guochen Zhang, Jundong Chen, Jiaqi Wang, Fan Zhao

    Published 2025-06-01
    “…Accurately detecting roses in UAV-captured greenhouse imagery presents significant challenges due to occlusions, scale variability, and complex environmental conditions. …”
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  20. 120

    Advanced lightweight deep learning vision framework for efficient pavement damage identification by Shuai Dong, Yunlong Wang, Jin Cao, Jia Ma, Yang Chen, Xin Kang

    Published 2025-04-01
    “…To address the demand for large-scale real-time pavement damage assessment, this study proposes a lightweight pavement damage detection model based on YOLOv5s (LPDD-YOLO). Initially, a lightweight feature extraction network, FasterNet, is adopted to reduce the number of parameters and computational complexity. …”
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