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王建柱,李清勇,张靖,甘津瑞(北京交通大学交通数据分析与挖掘北京市重点实验室, 北京 100044;电力系统人工智能国家电网公司联合实验室, 北京 102209)

摘 要
Visual inspection of rail defects: background, methodologies, and trends

Wang Jianzhu,Li Qingyong,Zhang Jing,Gan Jinrui(Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China;Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory, Beijing 102209, China)

As a national critical infrastructure, economic artery, and popular transportation, a railway plays an irreplaceable role in supporting the economic and social development of a nation. The rail is the key component of a railway, and correspondingly, rail defect detection is a core activity in railway engineering. Traditional manual inspection is time-consuming and laborious, and its results are easily influenced by various subjective factors. Therefore, automatic defect inspection for maintaining railway safety is highly significant. Considering the advantages of visual inspection in terms of speed, cost, and visualization, this study focuses on machine vision-based techniques. The track structure is first introduced by using the widely used ballastless track as an example. Sample presentation, causal analysis, and impact assessment of typical surface defects are provided. Then, the basic principles and application scenarios of common automatic rail defect detection technologies are briefly reviewed. In particular, ultrasonic techniques can be used to detect rail internal flaws, but it can hardly inspect fatigue damage on rail surface because of factors, such as ultrasonic reflection. Furthermore, detection speed is typically unsatisfactory. Eddy current inspection can obtain information about rail surface defects with the use of a detection coil by measuring the variance of eddy currents generated by an excitation coil. In contrast with ultrasonic technology, eddy current testing is fast and exhibits a distinct advantage in terms of detecting defects, such as shelling and scratch. However, it fails at finding defects that are located at the rail waist and base. Consequently, eddy current detection is frequently used in conjunction with ultrasonic equipment. Notably, eddy current inspection has high requirements for the installation position of the detection coil and the actual operation. Debugging the equipment is a complicated task, and the stability of the detection result is insufficient. Thereafter, current major challenges in the visual inspection of rail defects, namely, inhomogeneity of image qualities, limitation of available features, and difficulty in model updating, are summarized. Then, research actuality in the visual inspection of rail defects is systematically reviewed by categorizing the techniques into foreground, background, blind source separation, and deep learning-based models. One or two representative studies are elaborated in each category, followed by the analysis of technical features and practical limitations. In particular, foreground models typically suppress disturbing noise through operations, such as local image filtering, which can enhance contrast between the defect and the background, and thus, help recognize rail surface defects. This type of models generally exhibits low computational complexity, and thus, can meet the requirements of real-time inspection. However, they easily generate false positives and can hardly segment the defect target. Instead of directly placing emphasis on the defect, background methods model the image background by utilizing the spatial consistency and continuity of the rail image. Similar to foreground models, such methods also exhibit good real-time performance, but effectively decreasing false detection still requires further research. Blind source separation models detect rail defects on the basis of the low rank of the image background and the sparseness of the defect. Compared with the aforementioned two types of models, these approaches do not simply rely on the low-level visual characteristics of the defect target. However, these models tend to require high computational complexity. Deep learning-based models generally exhibit promising performance in the visual inspection of rail defects. However, training a deep learning model frequently requires a large amount of samples, and collecting and labeling numerous defect images can be costly. Moreover, these approaches typically depend on a dataset with specific supervision information, and thus, they may not perform well in other similar scenarios. Finally, future research trends in the visual inspection of rail defects are prospected by targeting the development requirements of smart railways. That is, technologies, such as few-shot or zero-shot learning, multitask learning, and multisource heterogeneous data fusion, should be explored to solve the problems of weak robustness and high false alarm rate existing in current visual inspection systems.