Shengtong Liu

The automatic inspection of real-time pantograph-and-catenary (PAC) operational status is important for ensuring the safety of the railway power supply system and realizing its intelligent operation and maintenance. However, state-of-the-art contact-loss monitoring methods cannot achieve high precision, high speed, and robust effects under complex conditions, such as frequent line changes, multi-suspended line interference, unknown complex backgrounds, and brightness variations. Therefore, a new two-stage online detection method for PAC operational status is proposed. First, we propose a three-module-assisted high-precision key point positioning detector for all possible intersection points of PAC. It can realize end-to-end PAC contact corner points detection for double branch contact mode in the process of line changes, instead of the original way of tracking and then detecting. Each auxiliary module adopts a minimalist ${3\times 3} $ convolutional layer without pre-training to enhance the feature expression ability of the corresponding layer in the backbone, thus improving the sub-pixel detection accuracy. Second, we propose a topological accuracy compensation algorithm to compensate for the mismatch error between the corner points in the image and the projection of the actual spatial contact point owing to the self-occlusion of PAC, thus improving the positioning precision of the real 3-D contact point. Physical simulation, accuracy verification, and field experiments prove that our PAC status inspection algorithm can achieve accurate conductor height and stagger value measurements under complicated environmental interference, thus ensuring the safety of train operation.