Semantic Segmentation of Transmission Lines and Their Accessories Based on UAV-Taken Images

While unmanned aerial vehicles (UAVs) facilitate transmission line inspection to some extent, they also generate numerous raw images rather than defect analysis results. Because analyzing the UAV-taken images by a human approach is an arduous work, an automatic method is needed to improve the analysis efficiency. In this paper, a framework is proposed to perform the image semantic segmentation of transmission lines and their accessories to generate the final defect detection results. A segment connection algorithm based on matrix operations is proposed to rapidly connect the segment features of objects. In line accessory detection, a background filter and an artificial contour segment feature generator are constructed to improve the detection performance. In addition, a distance threshold parameter automatic tuning mechanism is presented. Images provided by China Southern Power Grid Company taken by the UAVs are employed to validate the effectiveness of the proposed image semantic segmentation framework.