Integrating Multi-Features Fusion and Gestalt Principles for Pavement Crack Detection

Pavement cracks are often mixed with random particle textures on road surface and a variety of interference under natural environment, results in the crack detection method based on single feature cannot recognize real crack accurately. Therefore, this paper presents a novel pavement crack detection method through integrating multi-features fusion and Gestalt principles. It extracts the intensity differences, the probability of occurrence and edge property of cracks in multi-scale local regions as low-level salient features firstly. Then, according to the texture inhomogeneity and the spatial continuity of the irregular curvilinear structures of cracks, a novel texture anisotropy measure method(LFIA) is presented, which can weaken the disturbance of noisy points and pseudo-crack fragments efficiently. Based on the similarity, proximity and integrity principles of Gestalt theory, this paper adopts iterative clipping method to pre-segment LFIA map and proposes a crack spatial consistency enhancement strategy based on intra-regional and inter-regional connectivity to extract cracks. The experimental results of various collected pavement crack image database show the outstanding anti-noise performance and robustness. The precision and recall of our method is significantly superior to several existing conventional algorithms.