The detection of the railway track area in front of the train is a key link in active train collision avoidance technology. The existing railway area segmentation methods are mostly used for track detection in simple scenarios, posing challenges when confronted with complex scenarios such as railway switches in actual operation. We propose a method for detecting railway track areas for railway switches, which solves the problem that existing technology encounters difficulty in detecting the actual running area of trains under railway switches. First, a railway track area segmentation model based on information fusion is proposed. Aiming at the problem of difficulty in matching the left and right rails of the railway, the railway area and the rails are segmented and the segmentation results are used for rail matching. Second, a railway area reconstruction method based on inverse perspective transformation is designed to reconstruct the railway area by preserving the key points of the rails. Meanwhile, a railway switch classification model based on grouped convolution is used to identify the switch direction. Experimental results show that the proposed method achieves high accuracy in complex environments, with pixel accuracy (PA) index of 98.67% and Mean Intersection over Union (MioU) index of 98.12%, showcasing its potential applicability to trains.
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