Abstract Concrete sleepers are crucial infrastructure in railway construction and operation. Sleeper cracks may cause structural damage or functional failure of sleepers, thereby endangering railway operation safety. A third-order residual crack saliency detection network (R3Net CSD) model was constructed based on ConvNeXt for detecting concrete sleeper cracks in track inspection images, which is used to accurately extract the saliency features of rail sleeper crack images. The model first uses ConNeXt to form the backbone network and Block module to form the first residual, followed by a spatial pyramid (DCSP) module based on the anti residual structure and dilated convolution group to form the second residual, and finally uses the saliency residual (Att-R) module to form the third residual. At the same time, combined with a multi-level joint loss function supervision strategy for supervised learning, the model fully utilizes the low-level edge information and high-level semantic information of cracks. Using the images collected by the hand pushed rail inspection vehicle, a dataset of rail sleeper cracks was manually constructed and the model was experimentally validated. The experimental results show that this model achieved the best results in multiple indicators, such as an average absolute error MAE of 0.0082, an average accuracy AP of 0.9764, and an AUC value of 0.995. By combining the calculation method of crack geometric parameters, quantitative indicators of crack length and width were obtained, with a detection accuracy of 0.001mm. The research provides a new solution for the detection of cracks in concrete sleepers and the measurement of geometric parameters.
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