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首页> 外文期刊>IEEE transactions on industrial informatics >A Unified Light Framework for Real-Time Fault Detection of Freight Train Images
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A Unified Light Framework for Real-Time Fault Detection of Freight Train Images

机译：统一光框架，用于货运列车图像的实时故障检测

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摘要

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 is far from satisfactory in both accuracy and efficiency. This article proposes a unified light framework to improve detection accuracy while supporting a real-time operation with a low-resource requirement. We first design a novel lightweight backbone (real-time fault detection network-RFDNet) to improve the accuracy and reduce computational cost. Then, we propose a multiregion proposal network using multiscale feature maps generated from the RFDNet to improve the detection performance. Finally, we present multilevel 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 the 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/s and achieves competitive accuracy and lower computation than the state-of-the-art detectors.
机译：货运列车的实时故障检测在保证严格资源需求下保证铁路运输的安全性和最佳运行方面发挥着至关重要的作用。尽管基于深度学习的方法的有希望的结果，但货运车辆图像上这些故障探测器的性能远非令人满意的精度和效率。本文提出了一个统一的光框架，以提高检测准确性，同时支持具有低资源要求的实时操作。我们首先设计一种新颖的轻量级骨干（实时故障检测网络-RFDNET），以提高准确性并降低计算成本。然后，我们使用从RFDNET生成的MultiScale特征映射提出了一个多功能特征映射来提高检测性能。最后，我们提出了多级位置敏感的分数图和兴趣区域，以进一步提高冗余计算的准确性。公共基准数据集的广泛实验结果表明，我们的RFDNET可以显着提高基线网络的性能，具有更高的准确性和效率。六个故障数据集的实验表明，我们的方法能够在38帧以上的实时检测，并实现比最先进的探测器的竞争精度和更低的计算。

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