Substation Equipment Abnormity Detection Based on Improved YOLOv5

Yu Zhang, Weixing Li, Jian Zhou, Yan Gao

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

Substation equipment inspection is the basic work contents of equipment management and security. However, the substation equipment inspection is facing the contradiction between the rapid increase in the number of substation and the shortage of inspection personnel. We propose a deep learning-based method for detecting substation equipment abnormity to enhance the efficiency of operations. Based on YOLOv5, we add a small object detection head to solve the large scale variation of the equipment images. Besides, the K-means clustering algorithm is adopted to generate suitable anchors for training process, and we also perform data augmentation to increase the data complexity and numbers. Last, considering the overlapping area, center point distance and aspect ratio of the bounding box and ground truth, we use CloU loss function to replace the GIoU loss function. The experiments are executed on the industrial dataset, and the results reach 0.677 for ten types of equipment abnormities by the mAP. The experiment results show that the improved YOLOv5 algorithm is feasible and effective.

源语言英语
主期刊名Proceedings of the 41st Chinese Control Conference, CCC 2022
编辑Zhijun Li, Jian Sun
出版商IEEE Computer Society
6654-6659
页数6
ISBN(电子版)9789887581536
DOI
出版状态已出版 - 2022
活动41st Chinese Control Conference, CCC 2022 - Hefei, 中国
期限: 25 7月 202227 7月 2022

出版系列

姓名Chinese Control Conference, CCC
2022-July
ISSN(印刷版)1934-1768
ISSN(电子版)2161-2927

会议

会议41st Chinese Control Conference, CCC 2022
国家/地区中国
Hefei
时期25/07/2227/07/22

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引用此

Zhang, Y., Li, W., Zhou, J., & Gao, Y. (2022). Substation Equipment Abnormity Detection Based on Improved YOLOv5. 在 Z. Li, & J. Sun (编辑), Proceedings of the 41st Chinese Control Conference, CCC 2022 (页码 6654-6659). (Chinese Control Conference, CCC; 卷 2022-July). IEEE Computer Society. https://doi.org/10.23919/CCC55666.2022.9901958