  Fault detection for key components in the braking system of freight trains is
critical for ensuring railway transportation safety. Despite the frequently
employed methods based on deep learning, these fault detectors are highly
reliant on hardware resources and are complex to implement. In addition, no
train fault detectors consider the drop in accuracy induced by scale variation
of fault parts. This paper proposes a lightweight anchor-free framework to
solve the above problems. Specifically, to reduce the amount of computation and
model size, we introduce a lightweight backbone and adopt an anchor-free method
for localization and regression. To improve detection accuracy for multi-scale
parts, we design a feature pyramid network to generate rectangular layers of
different sizes to map parts with similar aspect ratios. Experiments on four
fault datasets show that our framework achieves 98.44% accuracy while the model
size is only 22.5 MB, outperforming state-of-the-art detectors.
