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The nonballasted rail tracks have been extensively applied in the new high‐speed railway system development in China, that is, the China Railway Track System (CRTS). However, many defects have been identified during the operation of the CRTS, among which concrete slab crack is recognized as one of the most common, yet critical defects that require accurate identification and timely maintenance attention. Because of the unique outlook of the cracks in nonballasted rail track slabs captured in the survey imagery, direct adaptations of the existing crack extraction methods show dramatically degraded performance. A new automatic crack identification method is developed in this study by employing a region‐based active contour framework with the intensity cluster energy. The proposed method embodies three major contributions, including (1) a heavy penalization energy component that could effectively avoid both under‐ and overevolutions; (2) a multiphase level‐set function that effectively evolves the contours generated with different intensity clusters; and (3) a two‐step implementation of the framework that significantly improves the efficiency. The experimental test evaluates the performance of the proposed method using the data collected on high‐speed rail tracks in Hebei Province, China. The proposed method accurately identifies more than 93.0% of digitized cracks in different crack patterns and challenging backgrounds using a data set consisting of 1,500 synthetic images and 150 actual images. In addition, it shows promising performance in comparison with other popular state‐of‐the‐art crack detection algorithms in terms of accuracy and computational efficiency. The proposed method has demonstrated the promising capacity to support a reliable and efficient nonballasted rail track crack identification and to facilitate the subsequent maintenance cost‐effectively.

Peak management and mean management are common ways to manage the quality of high-speed railway tracks at present. The most popular method for evaluating such tracks is the track quality index (TQI) method, which can reflect the overall state of the equipment to a certain extent. However, this method is likely to ignore some potential risks that threaten the operation of a high-speed train. For more effective risk identification, an incentive factor-based dynamic comprehensive evaluation (DCE) method was introduced to assess the geometric parameters of a high-speed railway track. Moreover, the weights of geometric parameters were computed by a combination of the analytic hierarchy process (AHP) and entropy based on the correlation coefficient. The proposed method can highlight the sensitivity index of the geometric parameters, which is an advantage over the TQI method. A case study of a high-speed railway track was performed using the two methods, and the results were verified with the original data. It was found that the TQI method identified only one obvious risk while the proposed method identified one obvious risk and two potential risks. This suggests that the proposed method is more accurate in identifying the risky sections than the TQI method.

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During the past two decades, subway systems have become one of the most dominant infrastructural developments in China at an unprecedented pace and scale. More than 60 metro lines in 25 cities have been completed, transporting more than 70 million passengers daily. Operating the subway systems safely and efficiently is a continuously pressing demand from both the management companies and the public. Although many automated or semi-automated methods for extracting critical components of the rail track systems, e.g. rail, fastener, sleeper, etc., have significantly improved the productivity of routine inspection, the unique challenges posed by the subway systems have hindered these existing methods from successful implementation because of the extremely low illumination in the underground environment, whereas additional artificial lighting often poses extremely uneven illumination. In this study, a generalized local illumination adaptation model using an anisotropic heat equation is proposed to dynamically adjust the acquired rail track images with extremely low and uneven illumination conditions. An integration flow is then proposed to seamlessly incorporate the proposed model into the state-of-the-art automated fastener detection algorithms. The results show that the proposed local illumination adaptation model can significantly improve the performance of the tested state-of-the-art fastener detection algorithms when they are applied to the images collected in the environment with extremely low and uneven illumination conditions, e.g. subway systems.