TriRNet: Real-Time Rail Recognition Network for UAV-Based Railway Inspection

UAVs have a broad application prospect in the field of railway inspection due to their excellent mobility and flexibility. However, it still faces challenges, such as high human labor costs and low intelligence levels. Therefore, it is of great significance to develop a real-time intelligent rail recognition algorithm that can be deployed on the onboard computing device to guide the UAV's camera to follow the target rail area and complete the inspection automatically. However, a significant issue is that rails from the perspective of UAVs may appear with changing pixel widths and various inclination angles. Concerning the issue, a general and adaptive rail representation method based on projection length discrimination (RRM-PLD) is proposed. It can always select the optimal representation direction, horizontal or vertical, to represent any kind of rails. With the RRM-PLD, a novel architecture (Real-Time Rail Recognition Network, TriRNet) is proposed. In TriRNet, a designed inter-rail attention (IRA) mechanism is presented to fuse local features of single rails and global features of other rails to accurately discriminate the geometric distribution of all rails in the image in a regressive way and thus improve the final recognition accuracy. Further, one-to-one mapping from anchor points to final feature maps is established. It greatly simplifies the model design process and improves the model's interpretability. Besides, detailed model training strategies are also presented. Extensive experiments have verified the effectiveness and superiority of the proposed formulation in terms of both network reasoning latency and recognition accuracy.