A fault detection method for transmission line components based on synthetic dataset and improved YOLOv5

输电线路 电力传输 计算机科学 块(置换群论) 故障检测与隔离 断层(地质) 过程(计算) 模式识别(心理学) 学习迁移 实时计算 工程类 人工智能 数学 电气工程 电信 操作系统 地质学 地震学 执行机构 几何学
作者
Jie Song,Xinyan Qin,Lei Jin,Jie Zhang,Yanqi Wang,Yujie Zeng
出处
期刊:International Journal of Electrical Power & Energy Systems [Elsevier BV]
卷期号:157: 109852-109852 被引量:11
标识
DOI:10.1016/j.ijepes.2024.109852
摘要

To address insufficient fault images for transmission line components (TLCs) and low accuracy in fault detection using deep learning techniques, we propose a fault detection method for TLCs based on synthetic datasets and improved YOLOv5. First, we introduce a synthetic image approach that uses prior information from the inspection process of a developed flying-walking power transmission line inspection robot (FPTLIR) to generate a synthetic dataset of fault components (SDFC). Second, we propose an improved YOLOv5 network called CSH-YOLOv5 to improve the accuracy of fault detection. The CSH-YOLOv5 network incorporates the convolutional block attention module (CBAM) and the latest SimCSPSPPF module to improve the detection accuracy of the network. In addition, a statistical analysis of small objects in the SDFC is performed and the neck and head of the YOLOv5 network are optimized accordingly to detect small objects. Finally, to address the lack of fault images in the unmanned aerial vehicle (UAV) inspection dataset, we implement a two-stage transfer learning strategy using the SDFC for training. We then experimentally evaluate the performance of the CSH-YOLOv5 network on a real test dataset. The results show that the CSH-YOLOv5 network achieves a mAP@[0.5] of 98.0% and a mAP@[0.5: 0.95] of 64.6% for fault detection, representing an improvement of 7.6% and 8.1%, respectively, over the YOLOv5 network. Comparative analysis indicates that the CSH-YOLOv5 outperforms other popular object detection networks, including Faster-RCNN, YOLOX, and YOLOv7 networks. The two-stage transfer learning strategy employed significantly enhances the network's generalization ability and detection accuracy on the UAV inspection dataset. The proposed method provides a technical reference for fault detection of TLCs, which can potentially benefit the power transmission industry.
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