  Real-time vision-based system of fault detection (RVBS-FD) for freight trains
is an essential part of ensuring railway transportation safety. Most existing
vision-based methods still have high computational costs based on convolutional
neural networks. The computational cost is mainly reflected in the backbone,
neck, and post-processing, i.e., non-maximum suppression (NMS). In this paper,
we propose a lightweight NMS-free framework to achieve real-time detection and
high accuracy simultaneously. First, we use a lightweight backbone for feature
extraction and design a fault detection pyramid to process features. This fault
detection pyramid includes three novel individual modules using attention
mechanism, bottleneck, and dilated convolution for feature enhancement and
computation reduction. Instead of using NMS, we calculate different loss
functions, including classification and location costs in the detection head,
to further reduce computation. Experimental results show that our framework
achieves over 83 frames per second speed with a smaller model size and higher
accuracy than the state-of-the-art detectors. Meanwhile, the hardware resource
requirements of our method are low during the training and testing process.
