Abstract
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 86.8% mAP@0.5 and 83.9% mAP@0.7, which was the best performance among all results.
Highlights
INTRODUCTIONHigh-speed railway transport has developed rapidly worldwide. The overhead contact system (OCS) is the key equipment for powering electric locomotives
In recent years, high-speed railway transport has developed rapidly worldwide
This paper proposes an improved Faster R-CNN for Overhead contact systems (OCSs) dropper detection, including the balanced attention feature pyramid network (BA-FPN) and center-point rectangle loss (CR loss)
Summary
High-speed railway transport has developed rapidly worldwide. The overhead contact system (OCS) is the key equipment for powering electric locomotives. 3) We use the improved Faster R-CNN as the basic object detection network and validate the proposed method on VOC 2012 [12], MSCOCO 2014 [13] and our OCS datasets. In order to address the impact of image complexity, we propose an attention-based feature fusion method combined with a high-precision Faster R-CNN network to form an effective object detector and realize dropper detection in complex backgrounds. Juan et al [32] proposed FB-NET detection based on a deep learning method for detecting the shape of railways and dangerous obstacles He et al [33] combined SSD and Faster-R-CNN to detect foreign matter in high-speed trains. The proposed method contains two aspects: a balanced attention feature pyramid network (BA-FPN) and a center-point rectangle loss (CR loss). The following process of the model is the same as FPN
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