• Laser & Optoelectronics Progress
  • Vol. 62, Issue 2, 0212004 (2025)
Fuzhen Huang* and Tianci Wang
Author Affiliations
  • College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
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    DOI: 10.3788/LOP241147 Cite this Article Set citation alerts
    Fuzhen Huang, Tianci Wang. Lightweight GCP-YOLOv8s for Insulator Defect Detection[J]. Laser & Optoelectronics Progress, 2025, 62(2): 0212004 Copy Citation Text show less
    References

    [1] Li C Y, Yao J M, Lin Z X et al. Object detection method based on improved YOLO lightweight network[J]. Laser & Optoelectronics Progress, 57, 141003(2020).

    [2] Guo Y, Ma M L, Li D L. Detection of surface defects in lightweight insulators using improved YOLOv5[J]. Laser & Optoelectronics Progress, 60, 2412007(2023).

    [3] Wang H Y, Wang C P, Fu Q et al. Lightweight ship detection based on optical remote sensing images for embedded platform[J]. Acta Optica Sinica, 43, 1212001(2023).

    [4] Huang Z, Wang Y Q, Wang H X et al. Design and application of UAV intelligent inspection system for transmission lines based on cloud and fog-edge heterogeneous collaborative computing architecture[J]. Electric Power, 53, 161-168(2020).

    [5] Li J N, Wang Z, Xu T F. Three-dimensional object detection technology based on point cloud data[J]. Acta Optica Sinica, 43, 1515001(2023).

    [6] Shao G W, Liu Z, Fu J et al. Research progress in unmanned aerial vehicle inspection technology on overhead transmission lines[J]. High Voltage Engineering, 46, 14-22(2020).

    [7] He Z F, Chen G C, Chen J S et al. Multi-scale feature fusion lightweight infrared pedestrian real-time detection at night[J]. Chinese Journal of Lasers, 49, 1709002(2022).

    [8] Liu K P, Li B Q, Qin L et al. Review on the application of deep learning target detection algorithm in insulator defect detection of overhead transmission lines[J]. High Voltage Engineering, 49, 2584-3595(2023).

    [9] He M, Qin L, Deng X L et al. MFI-YOLO: multi-fault insulator detection based on an improved YOLOv8[J]. IEEE Transactions on Power Delivery, 39, 168-179(2024).

    [10] Hu H T, Xu J, Huang Y et al. Insulator defect detection of transmission tower based on improved Faster R-CNN[J]. Information Technology and Informatization, 16, 63-66(2023).

    [11] Zuo Y, Liu W, Ma Y Q et al. Insulator defect detection based on improved GrabCut[J]. Computer Engineering and Design, 42, 2009-2015(2021).

    [12] Zha S K, Huang C R. A ConvNeXt and attention mechanism-based method for detecting spontaneous explosive faults in insulators[J]. Ningxia Electric Power, 42-50(2023).

    [13] Xiong E J, Zhang R F, Liu Y H et al. Ghost-YOLOv8 detection algorithm for traffic signs[J]. Computer Engineering and Applications, 59, 200-207(2023).

    [14] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C], 7464-7475(2023).

    [15] Huang T Y, Zhu J J, Liu Y et al. UAV aerial image target detection based on BLUR-YOLO[J]. Remote Sensing Letters, 14, 186-196(2023).

    [16] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [17] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

    [18] Han K, Wang Y H, Xu C et al. GhostNets on heterogeneous devices via cheap operations[J]. International Journal of Computer Vision, 130, 1050-1069(2022).

    [19] Howard A, Sandler M, Chen B et al. Searching for MobileNetV3[C], 1314-1324(2019).

    [20] Zhang X Y, Zhou X Y, Lin M X et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C], 6848-6856(2018).

    [21] Chen J R, Kao S H, He H et al. Run, don’t walk: chasing higher FLOPS for faster neural networks[C], 12021-12031(2023).

    [22] Liu X Y, Peng H W, Zheng N X et al. EfficientViT: memory efficient vision transformer with cascaded group attention[C], 14420-14430(2023).

    [23] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Computer vision-ECCV 2018, 11211, 3-19(2018).

    [24] Hu J, Shen L, Albanie S et al. Squeeze and Excitation Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

    [25] Li X, Zhong Z S, Wu J L et al. Expectation-maximization attention networks for semantic segmentation[C], 9166-9175(2019).

    [26] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

    [27] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Computer vision-ECCV 2016, 9905, 21-37(2016).

    [28] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).