Detection of Locomotive Signal Lights and Pedestrians on Railway Tracks Using Improved YOLOv4

Abstract

Intelligent detection of locomotive signal lights and pedestrians on railway tracks is of great significance to the safe operation of locomotives, especially under bad illumination conditions. Due to the highly complicated operation environment of railway locomotives, it is relatively difficult to apply deep neural network-based object detection methods in the recognition of locomotive signal lights and railway pedestrians. This work, for the first time, proposes a real-time detection method based on improved YOLOv4 to recognize locomotive signal lights and railway pedestrians. In our method, the Region-of-Interest is introduced into the original network of YOLOv4 to improve the detection precision of pedestrians on railway tracks. Importantly, we establish a dataset called detection of locomotive signal lights and railway pedestrians (DLSLRP), which is dedicated to the training, testing, and validation of related convolutional neural networks. We evaluated the proposed detector on DLSLRP dataset, the experimental results suggest that our method can detect locomotive signal lights and railway pedestrians with high speed and accuracy under different illumination conditions. The mAP reaches 93.52%, and the detection speed achieves 25 FPS.