Khan, M.A.-M.; Kee, S.-H.; Nahid, A.-A. Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model. Algorithms2023, 16, 568.
Khan, M.A.-M.; Kee, S.-H.; Nahid, A.-A. Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model. Algorithms 2023, 16, 568.
Khan, M.A.-M.; Kee, S.-H.; Nahid, A.-A. Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model. Algorithms2023, 16, 568.
Khan, M.A.-M.; Kee, S.-H.; Nahid, A.-A. Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model. Algorithms 2023, 16, 568.
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 the vision-based algorithm, 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 outperformed 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 (e.g., length, maximum width, area, density). 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.
Keywords
Crack Detection; Crack Quantification; Deep Learning; Dense U-net; Railway Sleeper
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
