Improved Road Crack Detection using Histogram Equalization based Fuzzy-C Means Technique

The road cracks are one of the major concerns in all over the world nowadays. It may cause road safety and increase the chance of accidents on the road. Every year a lot of the money is allocated for the maintenance and repair of the roads. This cost can be reduced if the cracks are early detected. If this detection performs manually then it is mostly dependent on labourers. The manual detection will consume more time and offer small accuracy. In the past, many algorithms have been developed to achieve the exact region of the crack. In this paper a novel technique will detect the cracks automatically by introducing Manhattan distance and histogram equalization in FCM called MHFCM. The crack detection from images is an intricate work due to background noise, scarce contrast, and intensity inhomogeneity. The experimental result shows that the novel segmentation technique provides the better result than the FCM and K-means for alligator, traverse and longitudinal cracks.