Transmission Line Foreign Body Fault Detection Using Multi-Feature Fusion Based on Modified YOLOv5. Issue 1 (1st August 2022)

• Record Type: Journal Article
• Authors: Wang, Qianyue; Si, Gangquan; Qu, Kai; Gong, Jiahui; Cui, Lele
• Is Part Of: Journal of physics. Volume 2320:Issue 1(2022)
• Publication Date: 2022-08-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2320/1/012028
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The particular work environment of transmission lines makes it liable to malfunction because of the foreign bodies attached to the power equipment. However, traditional detection methods are difficult to identify the exact category of foreign bodies and ignore the relationship between a foreign body and power equipment, which is correlated to determining whether there is a fault or not. In order to detect and distinguish the foreign bodies and give fault warnings automatically in transmission line patrol with high accuracy and efficiency, we improve the original YOLOv5 object detection model and propose a novel fusion detection model using multi-scale appearance and relationship features. The proposed model is capable of transmission line foreign body category detection and fault determination. By comparing with the original YOLOv5, the proposed model shows better accuracy and achieves the strong ability to give the fault determination simultaneously, which makes it convenient for power system operators and protects the safe and stable operation of the power system.