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EBS-YOLO: Foreign object detection algorithm for transmission lines based on improved Yolov10
S.X. Liu1, S.H. Qin2, D.Y. Jiang1

1 School of Electrical Engineering, Shanghai Dianji University, 201306, Shanghai, China, Shuihua Road 300;
1 The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions, Wuyi University, 354300, Fujian, China, Wuyi Avenue 16

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DOI: 10.18287/COJ1636

Article ID: 1636

Language: English

Abstract:
When a foreign body touches a transmission line it may have serious consequences. If it is not handled in time it may lead to accidents such as short circuits and blackouts, affecting the normal operation of the power system and the stability of social life. In order to detect foreign objects on transmission lines, this paper proposes an EBS-YOLO method based on Yolov10. Firstly, in the structure of the backbone network, we adopt C2f-Efficient Multi-Scale-Conv plus (C2f-EMSCP) as the convolutional layer for feature extraction, replacing part of the C2f standard convolutional layer, and obtaining a richer feature representation by combining different scales of feature mapping. Secondly, a Bidirectional Feature Pyramid Network (BiFPN) is used, which enables the model to fuse features of different scales better. Then, the SEAMHead module with multi-head attention is utilized to augment the original features, enhance head detection, and reduce the effect of object occlusion. Finally, in order to solve the consistency problem between the predicted and real bounding boxes, the SIoU loss is used to replace the original CIoU loss in Yolov10. The experimental results show that EBS-YOLO achieves an average detection accuracy of 90.4 %, which is 4.1 % better than Yolov10, and Recall and Precision are improved by 5.3 % and 1.8 %, respectively. Compared with other methods, our EBS-YOLO has higher accuracy in detecting foreign objects on transmission lines.

Keywords:
deep learning, Yolov10, transmission line foreign objects, multiscale, occlusion attention.

Citation:
Liu SX, Qin SH, Jiang DY. EBS-YOLO: Foreign object detection algorithm for transmission lines based on improved Yolov10. Computer Optics 2026; 50(1): 1636. DOI: 10.18287/COJ1636.

References:

  1. Zhibin Qiu, Xuan Zhu, Caibo Liao, Wenqian Qu, and Yanzhen Yu. A lightweight yolov4-edam model for accurate and real-time detection of foreign objects suspended on power lines. IEEE Transactions on Power Delivery, 38(2):1329--1340, 2022. DOI: 10.1109/TPWRD.2022.3213598.
  2. Paolo Sospiro, Lohith Amarnath, Vincenzo Di Nardo, Giacomo Talluri, and Foad H Gandoman. Smart Grid in China, EU, and the US: State of Implementation. Energies, 14(18):5637, 2021. DOI: 10.3390/en14185637.
  3. Minghu Wu, Leming Guo, Rui Chen, Wanyin Du, Juan Wang, Min Liu, Xiangbin Kong, and Jing Tang. Improved YOLOX foreign object detection algorithm for transmission lines. Wireless Communications and Mobile Computing, 2022(1):5835693, 2022. DOI: 10.2139/ssrn.4800586.
  4. Jinguo Zhu, Yue Guo, Fanding Yue, Huan Yuan, Aijun Yang, Xiaohua Wang, and Mingzhe Rong. A deep learning method to detect foreign objects for inspecting power transmission lines. IEEE Access, 8:94065--94075, 2020. DOI: 10.1109/ACCESS.2020.2995608.
  5. Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 580--587, 2014. DOI: 10.18127/j00338486-202109-11.
  6. Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6):1137--1149, 2016. DOI: 10.1109/TPAMI.2016.2577031.
  7. Kaiming He, Georgia Gkioxari, Piotr Dollар, and Ross Girshick. Mask R-CNN. In Proceedings of the IEEE International Conference on Computer Vision, pages 2961--2969, 2017. DOI: 10.7717/peerj-cs.1865.
  8. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. SSD: Single shot multibox detector. In Computer Vision -- ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part I 14, pages 21--37, 2016. DOI: 10.57152/malcom.v4i3.1332.
  9. X Han, J Chang, and KJPCS Wang. You only look once: Unified, real-time object detection. Procedia Computer Science, 183(1):61--72, 2021. DOI: 10.1109/CVPR.2016.91.
  10. Hui Li, Lizong Liu, Jun Du, Fan Jiang, Fei Guо, Qilong Hu, and Lin Fan. An improved YOLOv3 for foreign objects detection of transmission lines. IEEE Access, 10:45620--45628, 2022. DOI: 10.1109/ACCESS.2022.3170696.
  11. Shao Jia Li, Yan Xia Liu, Miao Li, and Lu Ding. DF-YOLO: Highly accurate transmission line foreign object detection algorithm. IEEE Access, 11:108398--108406, 2023. DOI: 10.1109/ACCESS.2023.3321385.
  12. Chunyang Liu, Lin Ma, Xin Sui, Nan Guo, Fang Yang, Xiaokang Yang, Yan Huang, and Xiao Wang. YOLO-CSM-Based Component Defect and Foreign Object Detection in Overhead Transmission Lines. Electronics, 13(1):123, 2023. DOI: 10.3390/electronics13010123.
  13. Chenhui Yu, Yakui Liu, Wanru Zhang, Xue Zhang, Yuhan Zhang, and Xing Jiang. Foreign objects identification of transmission line based on improved YOLOv7. IEEE Access, 11:51997--52008, 2023. DOI: 10.1109/ACCESS.2023.3277954.
  14. Jiangpeng Zheng, Hao Liu, Qiuting He, and Jinfu Hu. GEB-YOLO: a novel algorithm for enhanced and efficient detection of foreign objects in power transmission lines. Scientific Reports, 14(1):15769, 2024. DOI: 10.1038/s41598-024-64991-9.
  15. Yeqin Shao, Ruowei Zhang, Chang Lv, Zexing Luo, and Meiqin Che. TL-YOLO: Foreign-Object Detection on Power Transmission Line Based on Improved Yolov8. Electronics, 13(8):1543, 2024. DOI: 10.3390/electronics13081543.
  16. Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding. Yolov10: Real-time end-to-end object detection. arXiv preprint arXiv:2405.14458, 2024. DOI: 10.1007/s11554-022-01233-z.
  17. Mingxing Tan, Ruoming Pang, and Quoc V Le. Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10781--10790, 2020. DOI: 10.1109/CVPR42600.2020.01079.
  18. Ziping Yu, Hongbo Huang, Weijun Chen, Yongxin Su, Yahui Liu, and Xiuying Wang. Yolo-facev2: A scale and occlusion aware face detector. Pattern Recognition, 155:110714, 2024. DOI: 10.1109/cvpr42600.2020.01079.
  19. Zhora Gevorgyan. SIoU loss: More powerful learning for bounding box regression. arXiv preprint arXiv:2205.12740, 2022. DOI: 10.1016/j.neunet.2023.11.041.

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