Detection of Insulator Defects With Improved ResNeSt and Region Proposal Network

Shuqing Wang,Tianze Lan,Yifan Liu,Yihui Qing,Chenxi Wang,Ruotian Yao

doi:10.1109/access.2020.3029857

Abstract

Insulator is an important part of transmission line. Defective insulators will cause potential safety hazard to transmission lines. Image detection technology can improve the efficiency of insulator defect detection and greatly reduce the maintenance cost. However, the existing insulator defect detection technology has the disadvantages of low accuracy and long detection time. An insulator defect detection method based on improved ResNeSt and Region Proposal Network (RPN) was proposed. First, this method builds a new network based on ResNeSt. Secondly, we added the improved RPN to the improved ResNeSt for feature extraction, to better detect minor defects on insulators. Finally, we enhanced the data processing and labeled the open insulator data set. On this data set, the proposed model is tested and a large number of controlled experiments are done. The results show that the proposed network is more accurate and faster than the control group. Moreover, the proposed network has an accuracy rate of 98.38% for insulator defect detection, which can detect 12.8 pictures per second. The proposed method has good efficiency and practicability in aerial photo insulator defect detection.

Highlights

Insulator is an important part of transmission line, whose main function is electrical insulation and line support
The methods based on machine learning improve the accuracy of insulator location and defects detection, these methods have the common limitation of time-consuming because the sliding window strategy must be used to detect the whole aerial image
In this paper, a method of aerial photo insulator defect detection based on improved ResNeSt and improved multi-scale Region Proposal Network (RPN) is proposed

Summary

INTRODUCTION

Insulator is an important part of transmission line, whose main function is electrical insulation and line support. Compared with the original methods of insulator defects detection which are based on machine vision, the methods based on deep learning can extract the image features efficiently and automatically, which greatly improves the efficiency and accuracy of defects detection. In order to ensure the accuracy and speed of insulator defects detection, an improved ResNeSt [6] and Region Proposal Network (RPN) [7] is proposed.

RELATED WORKS

EXPERIMENTS AND DISCUSSION

Findings

CONCLUSION