Intelligent detection of catenary components based on an attention-enhanced Faster R-CNN

The catenary is a core structure of high-speed railway systems. It is responsible for providing a stable and reliable power supply to the train. In the process of train operations, catenary components are prone to failure. In order to ensure the safe operation of trains, it is essential to effectively detect and distinguish between different types of catenary component. In this paper, a catenary optical inspection method is proposed based on an attention-enhanced faster region-based convolutional neural network (Faster R-CNN). Firstly, the established Residual Network-50 (ResNet-50) is optimised by redesigning the bottleneck. In the bottleneck, a dual-path attentional enhancement mechanism is proposed by combining a parallel-convolutional block attention module (P-CBAM) in different paths. Secondly, a new adaptive attention module (NAAM) is designed to improve the small-sized object detection accuracy. Then, a feature pyramid network (FPN) is redesigned using the NAAM to reduce information loss during the feature map generation process and enhance the feature representation capability for multi-sized objects. Moreover, an exponential linear unit (ELU) activation function is introduced to improve the performance of the algorithm. In this paper, images of the whole catenary system are used. A single catenary image contains various components and eight of them are selected for analysis. Compared to other models, the proposed method achieves the highest detection accuracy with 6.6% improvement in mAP@0.5 and 4.78% improvement in mAP@0.5:0.95 over the baseline Faster R-CNN. Furthermore, the average recall (AR) also shows a notable enhancement. The experimental results confirm that the proposed method has good detection performance for distinguishing between different types of catenary component in electrified railways.