论文标题
货运火车中多尺度关键组件的视觉故障检测
Visual Fault Detection of Multi-scale Key Components in Freight Trains
论文作者
论文摘要
货运列车制动系统中关键组件的故障检测对于确保铁路运输安全至关重要。尽管经常采用基于深度学习的方法，但这些故障探测器高度依赖硬件资源，并且很复杂。此外，没有火车故障探测器考虑故障零件的比例变化引起的准确性下降。本文提出了一个轻巧的无锚框架来解决上述问题。具体来说，为了减少计算和模型大小的量，我们引入了轻巧的骨干，并采用无锚方法进行定位和回归。为了提高多尺度零件的检测准确性，我们设计了一个特征金字塔网络，以生成不同尺寸的矩形层以绘制具有相似纵横比的零件。四个故障数据集的实验表明，我们的框架达到98.44％的精度，而模型大小仅为22.5 MB，表现优于最先进的检测器。
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.
