LRE-Net: A Vision-Based real-time rail extraction method under Metro tunnel low-light scene

Abstract

Vision-based methods for detecting the dynamic clearance intrusion of trains in underground tunnels encounter challenges in extracting rails from low-contrast images caused by low-light scenes. This study introduces a solution for Rail Extraction in the Low-light scenes of underground tunnels (LRE-Net). Exposure features were integrated into the backbone to enhance the capability of the model in capturing global features in low-light scenes. Additionally, this study proxies the track extraction as a row-grid-based classification formulation. Two auxiliary tasks were introduced as regularization terms for generic feature learning during the training phase to address the potential overfitting resulting from the sparse nature of the track-extraction task. Experiments were conducted on a dataset consisting of 7,643 images, demonstrating that LRE-Net achieved the highest extraction accuracy of 93.94% at 88 frames per second (FPS). Furthermore, the impact of noise in low-light images on track extraction was analyzed, and strategies were proposed to enhance accuracy.