The cotter pin (CP) is a vital fastener for the catenary support components (CSCs) of high-speed electrified railways. Due to the vibration and excitation caused by the passing of railway vehicles, some CPs may be broken or fallen off over time, which poses a significant safety hazard to the railway systems. Currently, the CP defect detection is primarily conducted by humans, which is inefficient and inconsistent. Therefore, there is an urgent need for automatic CP defect detection to ensure railway safety. However, this task is very challenging as it requires covering hundreds or thousands of miles in limited times when the railway stops running. To this end, we first design a traffic track intelligent imaging device to capture catenary images at various angles at high speed. Then, inspired by the success of deep learning-based object detection, we develop a CP detection model based on an improved Faster R-CNN with a multi-scale region proposal network (MS-RPN) and propose the positive sample adaptive loss function (PSALF) to enhance detection accuracy. Finally, we propose a module to recognize the CP defect based on dilated convolution. The experimental results show that our method can effectively detect the CP defect in the catenary image, achieving 99.05 % precision and 98.40 % recall rate on CP defect detection. Furthermore, CP detection method and CP defect detection are significantly faster than baseline method, with FPS improvements of 2.76 and 24.67, respectively, thus making it more suitable for real-time applications in railway systems.

中文翻译：

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基于改进的 Faster R-CNN 和扩张卷积的电气化铁路开口销缺陷智能检测


开口销（CP）是高速电气化铁路接触网支撑部件（CSC）的重要紧固件。由于铁路车辆通过时产生的振动和激励，随着时间的推移，一些CP可能会破裂或脱落，这对铁路系统造成重大安全隐患。目前，CP缺陷检测主要由人工进行，效率低且不一致。因此，迫切需要自动检测CP缺陷以确保铁路安全。然而，这项任务非常具有挑战性，因为它需要在铁路停止运行的有限时间内行驶数百或数千英里。为此，我们首先设计了一种交通轨道智能成像装置，用于高速捕捉各种角度的悬链线图像。然后，受到基于深度学习的目标检测成功的启发，我们开发了一种基于改进的 Faster R-CNN 和多尺度区域提议网络（MS-RPN）的 CP 检测模型，并提出了正样本自适应损失函数（ PSLF）以提高检测精度。最后，我们提出了一个基于扩张卷积来识别 CP 缺陷的模块。实验结果表明，该方法能够有效检测悬链线图像中的CP缺陷，CP缺陷检测精度达到99.05%，召回率达到98.40%。此外，CP检测方法和CP缺陷检测明显快于基线方法，FPS分别提高了2.76和24.67，从而更适合铁路系统中的实时应用。