Internal Defect Detection of Overhead Aluminum Conductor Composite Core Transmission Lines With an Inspection Robot and Computer Vision

Abstract

Overhead aluminum conductor composite core (ACCC) transmission lines are extensively used. However, internal defects of ACCC wires are difficult to detect, threatening the stability and security of the grid. Thus, a novel automatic detection system using an X-ray inspection robot and an anchor-free object detection model is proposed to solve the problem of detecting internal defects in ACCC wires. First, a new inspection robot with a nondestructive testing (NDT) system consisting of a digital radiography (DR) detection panel and a portable X-ray generator is developed to acquire X-ray images of ACCC wires. Then, the IN-ACCC dataset is created by collecting the X-ray images of artificial defective ACCC wires and then processing, classifying, and labeling the images. Finally, an anchor-free object detection model named CenterNet-NDT is proposed based on CenterNet for high-performance identification of internal defects. CenterNet-NDT has a specially designed feature fusion module composed of spatial pyramid pooling-cross stage partial convolution (SPPCSPC), polarized self-attention (PSA), and a newly weighted bidirectional feature pyramid network named single-output weighted bidirectional feature pyramid network (SOFPN). Compared with some state-of-the-art methods and CenterNet with different modules, the proposed CenterNet-NDT achieves the highest mean average precision (mAP) of 90.60% on the IN-ACCC dataset. The proposed automatic internal defect detection system is verified to be effective and robust by lab experiments and has been repeatedly applied in actual ACCC transmission line inspection tasks to reduce the safety hazards of wire breakage.