U-Net-Based CNN Architecture for Road Crack Segmentation

Many studies on the semantic segmentation of cracks using the machine learning (ML) technique can be found in the relevant literature. To date, the results obtained are quite good, but often the accuracy of the trained model and the results obtained are evaluated using traditional metrics only, and in most cases, the goal is to detect only the occurrence of cracks. Particular attention should be paid to the thickness of the segmented crack since, in road pavement maintenance, the width of the crack is the main parameter and is the one that characterizes the severity levels. The aim of our study is to optimize the crack segmentation process through the implementation of a modified U-Net model-based algorithm. For this, the Crack500 dataset is used, and then the results are compared with those obtained from the U-Net algorithm, which is currently found to be the most accurate and performant in the literature. The results are promising and accurate, as the findings on the shape and width of the segmented cracks are very close to reality.

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