A Multilevel Feature and Structure Prior Information-Based Positioning Approach for Catenary Support Components

The work state of catenary support components (CSCs) is essential for trains’ powering safety under high-speed operation. However, because of the difficulty of positioning the CSCs whose sizes are much different, some detection methods based on classical deep learning technologies only position one specific component, and the others that can position all categories CSCs have poor performance. A new position method for 12 categories of CSCs called CSC structure inference net (CSCSIN) is proposed in this article. This method is composed of three sections, including the feature extraction with a multilevel feature, feature inference with structure prior information, regression, and classification. In the feature extraction section, to extract small-scale CSCs’ features, a modified VGG16, which is used to generate a legible feature map based on the fusion of low and high layers’ features, is proposed, and multiscales anchors for RoIs prediction are applied to suit multiscale CSCs. In the feature inference section, considering application scenarios and fixed position relationship between CSCs, the scene visual feature and position information between targets are used to iteratively update each target state in structure inference net, and the final state of each target is used for its classification in regression and classification section. Based on the modification in the feature extraction section and the combination of relevant information of CSCs, CSCSIN is efficient and sensitive to all 12 CSCs whose scales are much different. The experimental results demonstrate that the position precision of CSCSIN is superior to other classical models and relevant methods.