Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model

Abstract

Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly accidents. Traditional methods for detecting cracks on railway sleepers are very time-consuming and lack efficiency. Therefore, nowadays, researchers are paying attention to vision-based algorithms, especially Deep Learning algorithms. In this work, we adopted the U-net for the first time for detecting cracks on a railway sleeper and proposed a modified U-net architecture named Dense U-net for segmenting the cracks. In the Dense U-net structure, we established several short connections between the encoder and decoder blocks, which enabled the architecture to obtain better pixel information flow. Thus, the model extracted the necessary information in more detail to predict the cracks. We collected images from railway sleepers, processed them in a dataset, and finally trained the model with the images. The model achieved an overall F1-score, precision, Recall, and IoU of 86.5%, 88.53%, 84.63%, and 76.31%, respectively. We compared our suggested model with the original U-net, and the results demonstrate that our model performed better than the U-net in both quantitative and qualitative results. Moreover, we considered the necessity of crack severity analysis and measured a few parameters of the cracks. The engineers must know the severity of the cracks to have an idea about the most severe locations and take the necessary steps to repair the badly affected sleepers.

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Feature Sparse Choosing VIT Model for Efficient Concrete Crack Segmentation in Portable Crack Measuring Devices

“…He et al [17] proposed a CrackHAM model based on U-Net architecture, he designed a HASPP module and introduced a dual attention mechanism in his model to achieve accurate and robust crack segmentation results. Khan et al [18] established several short connections between the encoder and decoder blocks of the UNet model to enable the architecture to obtain better pixel information flow, named Dense-UNet. Gao et al [19] proposed an MRA-UNet, which uses a multi-scale residual module to capture fracture information at different scales on the down-sampling path, and adopted a plug-and-play dual attention module to recover features on the up-sampling path for crack segmentation.…”