Fuzzy C-Means clustering based selective edge enhancement scheme for improved road crack detection

One of the most serious problems currently in the world is road cracking, which could lower traffic safety and raise the risk of road accidents. A substantial sum of money is spent annually on maintaining and fixing the roads. However, manual crack detection is less accurate and takes longer. The focus of this study is on how artificial intelligence can be applied to image processing to improve crack detection. This study introduces the pixel classification enhancement fuzzy C-Means clustering, a new fuzzy C-Means clustering algorithm. The road crack detection from image is achieved automatically by utilizing second order pixel differences, an exponentially varying intensity dependent edge pixel enhancement scaling factor and intensity-based edge & non-edge fuzzy factors that helps to detect the cracks even in low contrast photos. This method doesn't necessitate training with image datasets. In addition, it can recognize edges or fractures in a range of unseen images. According to standard performance parameters, the experimental results demonstrate that the novel fuzzy C-Means segmentation-based approach performs better than many of the existing methods for contaminated or non-contaminated images in identifying patholes, transverse, and longitudinal cracks from road photos, as well as alligator cracks. The proposed method can assist managers or maintenance engineers in preventing further damage and quickly identifying different types of cracks with less manual labor and expense. •The PCEFCM has excellent effectiveness in identifying various types of road cracks, leading to substantial cost and time savings when compared to manual processes.•This improvement will make it possible to efficiently acquire and gather video feeds in real-time. Moreover, a suitable approach will emerge to determine the breadth and depth of road cracks.