(50-1) 16 *
<<
*
>>
* Русский *
English
*
Содержание *
Все выпуски
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;
2 The Key Laboratory of Cognitive Computing and Intelligent Information
Processing of Fujian Education Institutions, Wuyi University,
354300, Fujian, China, Wuyi Avenue 16
Полный текст (PDF)
DOI: 10.18287/COJ1636
ID статьи: 1636
Аннотация:
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.
Ключевые слова:
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:
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
Kaiming He, Georgia Gkioxari, Piotr Dollár, 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.
-
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.
-
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.
-
Hui Li, Lizong Liu, Jun Du, Fan Jiang, Fei Guo, 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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта:
journal@computeroptics.ru; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846)
332-56-22 (технический редактор), факс: +7 (846) 332-56-20