Abstract
Intelligent drone inspection of power lines using computer vision techniques is a very promising research topic. In particular, the detection of broken glass insulators due to their essential role in the proper functioning of electrical transmission lines. However, detection of such defects is challenging due to their small size coupled with complex aerial image backgrounds and limited dataset availability. In this regard, this paper uses advanced object detection algorithms to achieve real-time monitoring of broken glass insulators using drones by offering main contributions on two aspects. First, a large-scale aerial image dataset from Vietnam was meticulously constructed, including 1,010 labeled original images. It will serve as a valuable resource for those engaged in the automation of power line inspections. Second, a novel improved Yolov8 detection model is introduced, incorporating the Gather and Distribute mechanism for the fusion of different feature maps within the Neck component of Yolov8. Experimental results demonstrate that the model surpasses equivalent-scale models, such as Yolov8-m, Yolov7, RTDETR-l, Gold-Yolo and Yolov6-v3.0-m, exhibiting notable improvements. The model shows improvements of up to 2,1% in mean Average Precision mAP:50 while maintaining resource efficiency, with a smaller model size of at least 17.31% compared to other models, and it exhibits real-time performance capabilities. Overall, these promising contributions could advance the development of intelligent, reliable and automated anomaly detection systems suitable for drone inspections of power lines in the context of industry 4.0. Access to both the source code and the Vietnamese dataset is available through this link: https://github.com/phd-benel/Yolov8_gold.
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References
Liu, X., Miao, X., Jiang, H., Chen, J. (2020). Data analysis in visual power line inspection: an in-depth review of deep learning for component detection and fault diagnosis. Ann. Rev. Control, 50, 253–277. ISSN 1367–5788. [DOI: https://doi.org/10.1016/j.arcontrol.2020.09.002]
Wang, Z., Gao, Q., Xu, J., & Li, D. (2022). A Review of UAV Power Line Inspection. In: Yan L, Duan H, Yu X (eds) Advances in Guidance, Navigation and Control. Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. [DOI: https://doi.org/10.1007/978-981-15-8155-7_263
Xu, B., Zhao, Y., Wang, T., et al.: Development of power transmission line detection technology based on unmanned aerial vehicle image vision. SN Appl. Sci. 5, 72 (2023). https://doi.org/10.1007/s42452-023-05299-7
Li, Z., Wang, Q., Zhang, T., Ju, C., Suzuki, S., Namiki, A.: UAV high-voltage power transmission line autonomous correction inspection system based on object detection. IEEE Sens. J. 23(9), 10215–10230 (2023). https://doi.org/10.1109/JSEN.2023.3260360
Leite, L.R.P., Yanaguizawa, J.A., Shinohara, A.H., Costa, E.G., Xavier, G.J.V., Maciel, D.A.: Experimental study of electrical breakdown voltage of a glass insulator strings with different numbers of broken units. IEEE Int. Power Modulators High-Voltage Conf. 2008, 291–294 (2008). https://doi.org/10.1109/IPMC.2008.4743639
Jocher, G., Chaurasia, A., Qiu, J. (2023). YOLO by Ultralytics (Version 8.0.0) [Comput. Softw.]. https://github.com/ultralytics/ultralytics
Wang, C., et al.:(2023). Gold-YOLO: Efficient object detector via gather-and-distribute mechanism. ArXiv, abs/2309.11331
Tao, X., Zhang, D., Wang, Z., Liu, X., Zhang, H., Xu, D. (2018). Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Trans. Syst., Man, and Cybernetics: Systems
Lewis, D., Kulkarni, P.: Insulator defect detection. (2021). https://doi.org/10.21227/vkdw-x769
Roboflow Universe. The world’s largest collection of open source computer vision datasets. https://universe.roboflow.com/
Wang, G., Chen, Y., An, P., Hong, H., Hu, J., Huang, T.: UAV-YOLOv8: a small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios. Sensors 23, 7190 (2023). https://doi.org/10.3390/s23167190
Ma, M., Pang, H.: SP-YOLOv8s: an improved YOLOv8s model for remote sensing image tiny object detection. Appl. Sci. 13, 8161 (2023). https://doi.org/10.3390/app13148161
phd-benel. (2023). Broken Glass Insulator Dataset for UAV inspection of power lines. https://github.com/phd-benel/BGI
Project Overview. https://universe.roboflow.com/qbpcluoi110gd1/cdien_ttinh_ct_vo-nqghi
Cách điện thủy tinh bị vỡ bát trung thế Object Detection Dataset by DAKNONGPC. https://universe.roboflow.com/daknongpc/cach-dien-thuy-tinh-bi-vo-bat-trung-the
3cdien_ttinh_ct_vo Object Detection Dataset by QBPCLuoi110GD3. https://universe.roboflow.com/qbpcluoi110gd3/3cdien_ttinh_ct_vo
SUTHUYVO-MATBAT. https://universe.roboflow.com/daknongpc/suthuyvo-matbat
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: unified, real-time object detection. [Eprint]. arXiv, 1506.02640. [cs.CV]
Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection
Wang, C.-Y., et al. (2020). CSPNet: a new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 390–391)
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.(2018).Path Aggregation Network for Instance Segmentation.arXiv preprint arXiv:1803.01534.cs.CV
Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2017). Feature pyramid networks for object detection. In: IEEE Conf. Comput. Vis Pattern Recogn. (CVPR). https://arxiv.org/abs/1612.03144
C.S.E-Pathshala by Nirmal Gaud. (2023). YOLOv8 Object Detection Model. YouTube. https://www.youtube.com/watch?v=1hNojoj4In0&ab_channel=C.S.E-PathshalabyNirmalGaud
Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y. M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696
Lv, W., et al.: (2023).DETRs beat YOLOs on real-time object detection.arXiv preprint,arXiv:2304.08069
Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Wei, X. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976
phd-benel. (2023). Yolov8_gold (Version 1.0) [Source code]. GitHub. https://github.com/phd-benel/Yolov8_gold
Acknowledgments
This project is supported by the Research Foundation for Development and Innovation in Science and Engineering (FRDISI) and the Moroccan National Office of Electricity and Drinking Water (ONEE) in Casablanca, Morocco.
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Benelmostafa, BE., Aitelhaj, R., Elmoufid, M., Medromi, H. (2024). Detecting Broken Glass Insulators for Automated UAV Power Line Inspection Based on an Improved YOLOv8 Model. In: Ezziyyani, M., Kacprzyk, J., Balas, V.E. (eds) International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD'2023). AI2SD 2023. Lecture Notes in Networks and Systems, vol 930. Springer, Cham. https://doi.org/10.1007/978-3-031-54318-0_27
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DOI: https://doi.org/10.1007/978-3-031-54318-0_27
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