Detection and Evaluation Method of Transmission Line Defects Based on Deep Learning

The issues of existing research on transmission line detection include the following three: only detects a few categories, no open transmission line component dataset, and no unified, comprehensive evaluation index. In this paper, we propose a detection and evaluation method of defect for transmission line inspection based on deep learning. The transmission line contains various pivotal components, while previous research has mostly focused on a few categories. In the proposed approach, the following study is performed by establishing a transmission line dataset named Wire_10, which considers defects as a category. Wire_10 contains 8 defects in transmission line components, such as insulator defect, triple-plate defect, damper defect, grading ring defect, and et al., as well as nest and foreign body that attached to the transmission line. The object detection of aerial images taken during the actual inspection is susceptible to background and lighting. These two factors are used as variables to define the background-dataset and the lighting-dataset. Faster R-CNN, an end-to-end and high recognition accuracy deep learning algorithm, is used to build detection models with transfer learning and fine-tuning. The results show that the detection method can accurately identify the defect categories in the Wire_10 dataset and is robust to aerial images with complex backgrounds and different lighting. The proposed method can effectively and accurately identify defects in the automatic inspection of transmission lines.