Road crack detection using pixel classification and intensity-based distinctive fuzzy C-means clustering

Road cracks are quickly becoming one of the world's most serious concerns. It may have an impact on traffic safety and increase the likelihood of road accidents. A significant amount of money is spent each year for road repair and upkeep. This cost can be lowered if the cracks are discovered in good time. However, detection takes longer and is less precise when done manually. Because of ambient noise, intensity in-homogeneity, and low contrast, crack identification is a complex technique for automatic processes. As a result, several techniques have been developed in the past to pinpoint the specific site of the crack. In this research, a novel fuzzy C-means clustering algorithm is proposed that will detect fractures automatically by adding optimal edge pixels utilizing a second-order difference and intensity-based edge and non-edge fuzzy factors. This technique provides information of the intensity of edge and non-edge pixels, allowing it to recognize edges even when the image has little contrast. This method does not necessitate the use of any data set to train the model and no any critical parameter optimization is required. As a result, it can recognize edges or fissures even in novel or previously unknown input pictures of different environments. The experimental results reveal that the unique fuzzy C-means clustering-based segmentation method beats many of the existing methods used for detecting alligator, transverse, and longitudinal fractures from road photos in terms of precession, recall, and F1 score, PSNR, and execution time.