Foreign Body Detection in Rail Transit Based on a Multi-Mode Feature-Enhanced Convolutional Neural Network

Detection of railway traffic objects is an important task during train driving and is implemented to ensure safe driving. Although object detection has been investigated for years, many challenges exist in precisely detecting railway objects under complex railway scenes. These challenges mainly include adverse weather states, various railway backgrounds, diverse railway objects, and low-quality images. To address these issues, we introduce a novel deep learning method, called a multi-mode feature enhanced convolutional neural network (MMFE-Net), for accurate railway object detection. The network mainly consists of three modules. 1) An improved cross-stage partial connection darknet53 (CSPDarknet53), called adaptive dilated cspdarknet53, is used as our backbone to reduce image information loss. 2) A spatial feature extraction module is used to improve the feature extraction ability of the model for blurred objects and objects in a complicated background. 3) We introduce an attention fusion enhance module to strengthen the context information between adjacent feature maps to accurately detect multiscale and small objects. The proposed method achieves 0.9439 mAP and 79 FPS with an input size of 640x 640 pixels on the railway traffic dataset, and its performance is better than that of YOLOv4. Moreover, it is feasible to apply MMFE-Net into practical applications of railway object detection.

Automated summary

Detection of railway traffic objects is crucial for safe train driving. This study proposes a novel deep learning method, MMFE-Net, to accurately detect railway objects. The network utilizes improved backbone network, spatial feature extraction, and attention fusion enhance module to address challenges in complex railway scenes. Experimental results show that MMFE-Net outperforms other methods on railway traffic dataset and is feasible for practical railway object detection tasks.