Fault detection of railway freight cars mechanical components based on multi-feature fusion convolutional neural network

Abstract

The fault detection of the mechanical components in railway freight cars is important to the safety of railway transportation. Owing to the small size of the mechanical components, a manual detection method has a low detection efficiency. In addition, traditional computer vision technology has difficulty detecting multiple categories of objects simultaneously. Inspired by the use of one-stage deep-learning-based object detectors, in this paper, a multi-feature fusion network (MFF-net) for the simultaneous detection of three typical mechanical component faults is proposed. By embedding three modules in the network to improve the detection effect of small mechanical component faults, the feature fusion module is used to supplement the deep semantic information of the shallow feature maps. A multi-branch dilated convolution module uses dilated convolution and multi-branch networks to obtain the fusion features of multi-scale receptive fields, and the squeeze-and-excitation block is embedded in the network to enhance the channel features. All experiments used Nvidia 1080Ti GPUs for training on the PyTorch platform. The experimental results show that the three modules used in the network all contribute to the fault detection of railway freight car mechanical components, and that the detection performance of MFF-net is better than that of most other popular SSD-based one-stage object detectors. When the input image size is 300 pixels × 300 pixels, MFF-net can achieve 0.8872 mAP and 33 frames per second. It has good robustness to complex noise environment and can realize real-time fault detection of railway freight car mechanical components.