Pavement crack detection based on image saliency
基于图像显著性的路面裂缝检测

An effective approach for visual saliency detection can help people search for the object of interest from vast visual information rapidly and accurately. Considering the complexity of noises covering a wide area in actual road images, we present a new pavement crack detection approach based on image saliency in this paper. This approach calculates the salient value of crack images in a coarse scale based on the grayscale sparsity and global contrast after grayscale correction on images, which are divided into small blocks. Then, according to the characteristics of the cracks, such as local brightness, edge, and continuity, we calculate the local saliency in the continuously outspread local neighbor domain in a fine scale. After enhancing the saliency based on the spatial continuity, we extract cracks using adaptive image segmentation method. A large number of experimental results demonstrate this approach can detect the crack areas more correctly and effectively compared with traditional methods. It better suppress noises, has lower missing rate and misuse detection rate. Moreover, the result is consistent with human visual characteristics.