Three-Dimensional Point Cloud Stitching Method in Infrared Images of High-Voltage Cables

High-voltage power cables are crucial to the normal operation of all electrical equipment. The insulation surrounding these cables is subject to faults. The traditional methods for detecting cable insulation characteristics primarily focus on breakdown performance tests. However, the measurement precision is low, the risk coefficient is high, and the test cost is high. Additionally, it is difficult to precisely pinpoint high-voltage cable faults. Therefore, in this study, a method for inspecting high-voltage cable faults using infrared stereoscopic vision is proposed. This method enables non-contact remote safety measurements to be conducted. For a limited lens angle in an infrared camera, an area matching stitching method that incorporates feature point matching is developed. The key technologies for three-dimensional (3D) point cloud stitching include feature point extraction and image matching. To address the problem of the Harris algorithm not having scale invariance, Gaussian multi-scale transform parameters were added to the algorithm. During the matching process, a random sampling consistency algorithm is used to eliminate incorrect pairs of matching points. Subsequently, a 3D point cloud stitching experiment on infrared cable images was conducted. The feasibility of the stitching algorithm was verified through qualitative and quantitative analyses of the experimental results. Based on the mechanism by which thermal breakdowns occur, a method for detecting anomalous temperatures in cables is developed based on infrared stereoscopic vision. In this manuscript, the infrared technique, 3D point cloud stitching, and cables inspection are combined for the first time. The detection precision is high, which contributes to the development of high-voltage electrical equipment nondestructive testing.