In high-speed railways, contact forces between the pantograph and the overhead catenary system (OCS) are substantial. Vibration and excitation propagated from the vehicle-track and the pantograph-OCS interactions would progressively damage fasteners, including but not limited to, loose bolts, cracked components, and missing parts. Existing automatic detection methods typically rely on a three-stage approach, of which the first two stages focus on locating joints and fasteners while the last stage focuses on the detection. Due to the nature of the three-stage detector, the computational cost is high, and the inspection speed is low. This study proposes an innovative two-stage method with two improved convolutional neural network (CNN)-based networks, cascade YOLO (You Only Look Once) and Rotation RetinaNet (RRNet). The proposed method was compared to traditional horizontal anchor-based methods and other methods. The results demonstrate the proposed method outperforms other methods in terms of accuracy, while maintaining a reasonably high processing speed.

在高速铁路中，受电弓与架空接触网系统 (OCS) 之间的接触力很大。从车辆-轨道和受电弓-OCS 相互作用传播的振动和激励会逐渐损坏紧固件，包括但不限于螺栓松动、部件破裂和零件缺失。现有的自动检测方法通常依赖于三阶段方法，其中前两个阶段侧重于定位接头和紧固件，而最后阶段侧重于检测。由于三级检测器的性质，计算成本高，检测速度低。本研究提出了一种创新的两阶段方法，其中包含两个改进的基于卷积神经网络 (CNN) 的网络，级联 YOLO（您只看一次）) 和旋转 RetinaNet (RRNet)。将所提出的方法与传统的基于水平锚的方法和其他方法进行了比较。结果表明，所提出的方法在准确性方面优于其他方法，同时保持了合理的高处理速度。