Real-Time Railway Obstacle Detection Based on Multitask Perception Learning

Railway object intrusion poses a significant threat to railway safety, so it is vital to monitor the obstacles within the track area in real-time to prevent accidents, which can be achieved by vision-based technologies. However, most existing vision-based railway obstacle detection algorithms are limited to stationary cameras, which restricts the monitoring range. In contrast, onboard approaches enable full-line monitoring but face more challenges in achieving high accuracy and real-time performance. To address these challenges, we developed an onboard end-to-end multitask perception model (MTP-Rail), which includes variants of different scales (S, M, L) to realize railway obstacle detection in real-time with high accuracy. Our model consists of two decoders, which can simultaneously implement the tasks of object of interest detection and track segmentation. In addition, we designed a post-processing scheme to analyze the relationship between the detected object and the track and assess the obstacle risk level. Experiments on our dataset show that one of the proposed model MTP-rail-M achieved an 89.6% classification accuracy of obstacle risk level and 54.3% on mAP, 78.6% on mPA and 64.9% on mIoU with an inference speed of 181 FPS at an input size of $640\times 640$ on RTX 3060Ti. Our model is easy to install and deploy, highlighting its potential in engineering applications. The code is available on https://github.com/ccl-1/obstacle_detection.