This article mainly focuses on the most common types of high-speed railways malfunctions in overhead contact systems, namely, unstressed droppers, foreign-body invasions, and pole number-plate malfunctions, to establish a deep-network detection model. By fusing the feature maps of the shallow and deep layers in the pretraining network, global and local features of the malfunction area are combined to enhance the network's ability of identifying small objects. Further, in order to share the fully connected layers of the pretraining network and reduce the complexity of the model, Tucker tensor decomposition is used to extract features from the fused-feature map. The operation greatly reduces training time. Through the detection of images collected on the Lanxin railway line, experiments result show that the proposed multiview Faster R-CNN based on tensor decomposition had lower miss probability and higher detection accuracy for the three types faults. Compared with object-detection methods YOLOv3, SSD, and the original Faster R-CNN, the average miss probability of the improved Faster R-CNN model in this paper is decreased by 37.83%, 51.27%, and 43.79%, respectively, and average detection accuracy is increased by 3.6%, 9.75%, and 5.9%, respectively.

本文主要针对高架接触系统中最常见的高速铁路故障类型，即无应力的滴管，异物侵入和极板号码故障，建立一个深层网络检测模型。通过融合预训练网络中浅层和深层的特征图，可以组合故障区域的全局和局部特征，以增强网络识别小物体的能力。此外，为了共享预训练网络的完全连接的层并降低模型的复杂性，塔克张量分解用于从融合特征图中提取特征。该操作大大减少了培训时间。通过检测在兰新铁路线上收集的图像，实验结果表明，提出的基于张量分解的多视图Faster R-CNN对三种类型的故障具有较低的遗漏概率和较高的检测精度。与对象检测方法YOLOv3，SSD和原始Faster R-CNN相比，改进的Faster R-CNN模型的平均未命中率分别降低了37.83％，51.27％和43.79％，并且平均检测准确度分别提高了3.6％，9.75％和5.9％。