Overhead contact systems (OCSs) are the power supply facility of high-speed trains and plays a vital role in the operation of high-speed trains. The dropper is an important guarantee for the suspension system of the OCS. Faults of the dropper, such as slack and breakage, can cause a certain threat to the power supply system. How to use artificial intelligence technologies to detect faults is an urgent technical problem to be solved. Because droppers are very small in whole images, a feasible solution to the problem is to identify and locate the droppers first, then segment them, and then identify the fault type of the segmented droppers. This paper proposes an improved Faster R-CNN algorithm that can accurately identify and locate droppers. The innovations of the method consist of two parts. First, a balanced attention feature pyramid network (BA-FPN) is used to predict the detection anchor. Based on the attention mechanism, BA-FPN performs feature fusion on feature maps of different levels of the feature pyramid network to balance the original features of each layer. After that, a center-point rectangle loss (CR Loss) is designed as the bounding box regression loss function of Faster R-CNN. Through a center-point rectangle penalty term, the anchor box quickly moves closer to the ground-truth box during the training process. We validate the improved Faster R-CNN through extensive experiments on the VOC 2012 and MSCOCO 2014 datasets. Experimental results prove the effectiveness of the proposed network combined with attention feature fusion and center-point rectangle loss. On the OCS dataset, the accuracy using the combination of the improved Faster R-CNN and ResNet-101 reached 86.

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An overhead contact system (OCS) is key to providing power to high-speed railways. OCS detection is an important measure to ensure the safe operation of a high-speed railway. At present, OCS anomaly detection mainly relies on the manual analysis of the images regularly collected by the 4C system, which is very inefficient and can easily miss anomalies. Although some classification and object detection methods based on deep learning can be used for OCS anomaly detection, the effective training of deep networks can be difficult to support due to the small number of anomaly OCS image samples. Considering that most OCS faults are abnormal fasteners, we propose an abnormal detection method based on normal images, called the nested residual encoder-decoder network (NRE-Net). This network consists of two nested encoder-decoder networks, where the encoder is the shared part, and a residual structure is added to the encoding and decoding branches to enhance the feature expression ability. The experimental results show that the method can greatly improve the accuracy of anomaly detection for the CIFAR-10 dataset and OCS fastener dataset.