  Real-time fault detection for freight trains plays a vital role in
guaranteeing the security and optimal operation of railway transportation under
stringent resource requirements. Despite the promising results for deep
learning based approaches, the performance of these fault detectors on freight
train images, are far from satisfactory in both accuracy and efficiency. This
paper proposes a unified light framework to improve detection accuracy while
supporting a real-time operation with a low resource requirement. We firstly
design a novel lightweight backbone (RFDNet) to improve the accuracy and reduce
computational cost. Then, we propose a multi region proposal network using
multi-scale feature maps generated from RFDNet to improve the detection
performance. Finally, we present multi level position-sensitive score maps and
region of interest pooling to further improve accuracy with few redundant
computations. Extensive experimental results on public benchmark datasets
suggest that our RFDNet can significantly improve the performance of baseline
network with higher accuracy and efficiency. Experiments on six fault datasets
show that our method is capable of real-time detection at over 38 frames per
second and achieves competitive accuracy and lower computation than the
state-of-the-art detectors.
