Self-adaptive rail edge detection for trams based on mathematical morphology.

With the growing number of tram operation lines, tram-related traffic incidents, particularly train collisions, have become a major issue. Therefore, the ability to identify foreign objects on a track is critical to tram operational safety. Accurately identifying the rail edge is a critical technology for recognizing the track area and providing early warnings of potential threats. Therefore, this study proposes a self-adaptive rail-edge detection algorithm that uses mathematical morphology and computer vision technology to accurately extract rail edges. The performance of the proposed algorithm was compared to that of existing algorithms, including the Canny algorithm and two other methods described in publication. Three scenes in the level crossing area of trams were considered as the research objects, and the effects of two types of noise in the image were explored in terms of the investigated using mean square error (MSE), peak signal-to-noise ratio (PSNR), and computational time. The results showed that the proposed model exhibited strong robustness for different scenes, particularly in the presence of noise. This suggests that the proposed algorithm could be used in early warning systems of trams to identify rail edges.