Point cloud segmentation of overhead contact systems with deep learning in high-speed rails

Abstract

High-speed rails have strategic significance for political and economic development. As a critical part of the power supply system, overhead catenary systems (OCSs) power high-speed rails. OCSs are easily damaged due to artificial and natural factors. To ensure that OCSs work properly, researchers often use light detection and ranging (LiDAR) to recognize and inspect OCS components. However, recent OCS recognition methods relying on LiDAR need to enhance the ability to extract local features, integrate contextual features, and enhance crucial features while maintaining less latency and model complexity. To accomplish this objective, we proposed an accelerated point cloud segmentation algorithm for OCS recognition. The algorithm extracted local point cloud features based on machine learning methods. Then, it integrated the contextual features of point clouds using dot and accumulation operations. Additionally, we captured vital features and neglected unimportant features by constructing a feature enhancement algorithm. We accelerated the proposed algorithms by eliminating the run-time overhead of GPU scheduling. Experiments showed that our method had high precision with low model complexity and latency. For example, our precision was at least 0.64% better than comparison studies in recognizing steady arms. Our parameters were at least 43.59% fewer than others. Our optimized algorithm achieved a 2.47, 2.81, 3.24, 1.54, 7.12, and 7× speedup on the Nano, Tesla T4, RTX 2080 Ti, TX2, Tesla V100S GPU, and TITAN V, respectively. Our visualization effect also outperformed other methods in OCS recognition of high-speed rail scenarios.