arXiv 1802.012862018-02-05cs.CV

轨道检测用铁路图像的数据增强

Data Augmentation of Railway Images for Track Inspection

General Electric（通用电气）

S Ritika, Dattaraj Rao

摘要：

所有资产的定期维护对于铁路的正常运行至关重要。手动维护可能非常麻烦，并为错误留下空间。轨道异常，如植被过度生长，太阳扭结影响轨道结构，并导致不平等的负载转移，不平衡的横向力的轨道上，导致进一步恶化的轨道，并最终导致机车脱轨。因此，需要持续监测轨道健康状况。轨道异常是罕见的倾斜高达一个异常，在数百万的良好的图像。我们提出了一种方法来建立训练数据，这将使我们的算法更强大，并帮助我们检测现实世界的轨道问题。数据增强将对我们更好地检测异常产生直接影响，从而缩短铁路人工检查的时间。本文讨论了一个现实世界的用例，从安装在移动机车上的摄像头检测铁路轨道缺陷并跟踪它们的位置。该摄像机的设计可以承受移动训练上的环境因素，并以每秒30帧左右的速度提供一致稳定的图像。实现了轨迹检测、感兴趣区域选择、异常图像增强的图像仿真流水线。训练图像模拟太阳扭结和植被过度生长。在Imagenet数据集上预训练的Inception V3模型针对2类分类进行了微调。对于植被过度生长的情况，该模型在实际植被图像上具有很好的泛化能力，尽管它仅在可能与实际植被分布不同的模拟图像上进行训练和验证。太阳扭结分类器可以对专业模拟的太阳扭结视频进行分类，准确率为97.5%。

英文摘要：

Regular maintenance of all the assets is pivotal for proper functioning of railway. Manual maintenance can be very cumbersome and leave room for errors. Track anomalies like vegetation overgrowth, sun kinks affect the track construct and result in unequal load transfer, imbalanced lateral forces on tracks which causes further deterioration of tracks and can ultimately result in derailment of locomotive. Hence there is a need to continuously monitor rail track health. Track anomalies are rare with the skew as high as one anomaly in millions of good images. We propose a method to build training data that will make our algorithms more robust and help us detect real world track issues. The data augmentation will have a direct effect in making us detect better anomalies and hence improve time for railroads that is spent in manual inspection. This paper talks about a real world use case of detecting railway track defects from a camera mounted on a moving locomotive and tracking their locations. The camera is engineered to withstand the environment factors on a moving train and provide a consistent steady image at around 30 frames per second. An image simulation pipeline of track detection, region of interest selection, augmenting image for anomalies is implemented. Training images are simulated for sun kink and vegetation overgrowth. Inception V3 model pretrained on Imagenet dataset is finetuned for a 2 class classification. For the case of vegetation overgrowth, the model generalizes well on actual vegetation images, though it was trained and validated solely on simulated images which might have different distribution than the actual vegetation. Sun kink classifier can classify professionally simulated sun kink videos with a precision of 97.5%.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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