Real-Time Accurate Deep Learning-Based Edge Detection for 3-D Pantograph Pose Status Inspection

Abstract

As an important part of the online monitoring in electrified railway, pantograph operational pose inspection plays a vital role in pantograph and catenary system (PCS) security. Due to the impact of the complex conditions on the train roof, it is difficult to replace the existing video surveillance with fully intelligent operational pose inspection. Therefore, this article proposes a novel accurate monocular 3-D pantograph horn edge-based pantograph pose inspection method in the power of superior ability of deep learning. It mainly includes two steps: edge detection and pose optimization. First, we highlight a brand-new high-precision subpixel edge prediction network. The downsampling heatmap and offset prediction map in our edge detector are joined to predict full-size edge points and thus improve the accuracy to the subpixel level. In addition, our newly proposed numerical gradient smoothing loss function overcomes the “jaggy” phenomenon of the edge curves. Second, we propose a nonpoint-to-point corresponding 3-D pose measurement method of the monocular camera. In the iterative optimized process, we innovatively apply the chamfer distance as an optimization function to dynamically adjust the correspondence between the reprojection points and the predicted edge points to optimal convergence. Ablation and comparative experiments demonstrate that our method achieves excellent performance (F-score 93.0% in FEP_2.0 (fixed edge probability) with 82 frames per second (FPS), overcoming the complex conditions of the practical online monitoring sequences. The actual pose measurement results further confirm our efficiency, thereby achieving a robust real-time and high-precision monocular 3-D pantograph operating pose inspection.