Autonomous Railway Traffic Object Detection Using Feature-Enhanced Single-Shot Detector

Abstract

With the high growth rates of railway transportation, it is extremely important to detect railway obstacles ahead of the train to ensure safety. Manual and traditional feature-extraction methods have been utilized in this scenario. There are also deep learning-based railway object detection approaches. However, in the case of a complex railway scene, these object detection approaches are either inefficient or have insufficient accuracy, particularly for small objects. To address this issue, we propose a feature-enhanced single-shot detector (FE-SSD). The proposed method inherits a prior detection module of RON and a feature transfer block of FB-Net. It also employs a novel receptive field-enhancement module. Through the integration of these three modules, the feature discrimination and robustness are significantly enhanced. Experimental results for a railway traffic dataset built by our team indicated that the proposed approach is superior to other SSD-derived models, particularly for small-object detection, while achieving real-time performance close to that of the SSD. The proposed method achieved a mean average precision of 0.895 and a frame rate of 38 frames per second on a railway traffic dataset with an input size of $320\times320$ pixels. The experimental results indicate that the proposed method can be used for real-world railway object detection.