A Topology Guided Method for Rail-Track Detection

Abstract

Rail-track detection is a core function for automated rail transit perception. However, existing methods cannot effectively detect the rail-tracks in a complex environment, especially in turnout scenarios. In this study, we propose a topology guided method to detect rail-tracks which includes the following four parts: Firstly, a neural network is used to obtain the pixels of the rail-lanes, and the geometric relationship between rail-lanes is mined by inverse perspective transformation. Secondly, the rail-lanes’ pixels are converted to rail-lanes’ key points and the topological relationship between the key points. Thirdly, the rail-lanes are reconnected through topological relationships between key points. Finally, the rail-track geometry features are used to match the rail-lanes. Experimental results show that the rail-track level F1 score of the proposed method reached 91.62%, which is state-of-the-art (SOTA) in this field. Furthermore, the proposed method has been tested and applied on the Hong Kong Metro Tsuen Wan Line.