A Computer Vision System for Power Transmission Line Inspection Robot

Periodic inspections of power grids to prevent power outages and promptly handle potential risks are one of the most important tasks of the electricity industry. However, this is a tedious, time-consuming and dangerous job as all current inspection methods are carried out manually. This paper proposes a computer vision system that assists inspection robots working on overhead high voltage transmission lines to operate autonomously. Our system performs three main functions. The first is to detect obstacles by using YOLOv4, a state-of-the-art object detection technique so that the robot can determine how to properly overcome obstacles. The second is to estimate the distance to the obstacle by using linear regression technique so that the robot can determine the exact time to overcome the object. The third is to detect wire defects based on the wire edges by using image processing techniques. Our achieved performance of the system: detecting obstacles with mAP@0.5 equal to 98.65%, estimating distance to objects with average mean absolute error equal to 0.81cm in the range from 20cm to 100cm, and detecting wire defects with precision equal to 90.24% and recall equal to 86.05%. Our computer vision system is accurate and reliable, ready to integrate with the robot in real life. Inspection robots with this system will make the inspection of power lines faster and simpler, which saves time, maintenance costs and labor.