Pantograph-catenary contact point detection method based on image recognition

A two-stage fast detection method was proposed aiming at the poor real-time performance and low accuracy of existing pantograph-catenary contact points detection methods. In the first stage, a pantograph-catenary region segmentation algorithm was proposed based on the improved BiSeNet v2. The shallow feature sharing mechanism was used to send the shallow features extracted from the detail branch to the semantic branch to obtain the high-level semantic information and reduce the redundant parameters. The Squeeze-and-Excitation Attention Mechanism was embedded into the network model to enhance the important channel information. The Pyramid Pooling Module was added to obtain the multi-scale features to improve the accuracy of the model. In the second stage, based on the segmentation results, contact points detection was achieved by the linear fitting and the position correction. The experimental results showed that the proposed segmentation algorithm had an accuracy of 87.50%, floating point operations of 6.73 G, and an inference speed of 49.80 frames per second and 12.60 frames per second on CPU (Intel Core I9-12900) and JETSON TX2. The proposed detection method was experimented in the pantograph-catenary simulation platform and the pantograph-catenary system of the dual-source intelligent heavy truck. The experimental results showed that the method can effectively detect the contact points of the pantograph-catenary. © 2024 Zhejiang University. All rights reserved.