An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection

Among the various defects of asphalt pavement distress, much attention has been paid to cracks which often cause significant engineering and economic problems. Crack detection is not an easy task since images of road pavement surface are very difficult to analyze. In this paper, a highly efficient pavement crack detection system is proposed, which has the following distinguishing features. Firstly, a new description of the cracks is proposed based on the spatially clustered pixels with similar gray levels. Secondly, an adaptive thresholding method is presented for image segmentation by comprehensively taking into account the spatial distribution, intensities and geometric features of cracks. Thirdly, a new concept termed Region of Belief (ROB) is introduced to facilitate the subsequent detection by defining some credibility factors which indicate the reliability that a region could be labeled as a distress region which contains cracks, and an algorithm to extract such ROBs is devised accordingly. Lastly, a novel region growing algorithm is propounded for crack detection, which features starting with an ROB seed, determining the searching scope with a specially devised rule, and searching and merging a ROB with different regions following a similarity criterion which synthetically takes different cues into consideration. Two different types of experiments were conducted. The first one was carried out using 10,000 of our field-captured images which were taken from different road conditions and environments. The second one was completed using a benchmark dataset for a comparison with other recent publications. The evaluation performance is satisfactory for a variety of different cracks. For our own data, the detection accuracy is over 95% and more than 90% of coherent cracks without disconnected fragments have been correctly detected as the integrated ones. For the benchmark data, our detection performance also outperforms previously published results. Currently, our approach has been widely applied in China.