Automatic crack detection algorithm for airport pavement based on depth image

To detect small cracks in airport pavements under strong noise, weak illumination and low contrast, a crack detection algorithm for airport pavements based on depth images was designed. The collected depth image was divided into multiple grids, and each grid was expanded to obtain a local pavement region. For each grid region, the random sampling consensus algorithm was used to construct and optimally estimate the local cubic curved surface. On this basis, the global curved surface model of the entire image acquisition region of parement was generated by fusing the curved surface models of all grid regions under the global scale. Based on the difference image between the global curved surface model and the original depth image, candidate crack pixels were segmented by the adaptive threshold method, and various morphological constraints, such as the total number, length and length width ratio of crack pixels were used to screen the candidate crack pixels to eliminate the incorrect candidate crack pixels, so as to obtain the final crack detection results. The experiment was carried out on the airport pavement depth image datasets. The manual annotation results were taken as the ground truth. The accuracy, recall rate and F value were used as the quantitative evaluation indices. The proposed algorithm was compared with four representative traditional algorithms. Experiment result shows that the highest accuracy, recall rate and F value of the traditional algorithm are 77.05%, 41.02% and 50.02%, respectively. The proposed algorithm has obvious advantages in the accuracy, recall rate and F value, with average values of 91.20%, 97.99% and 94.12%, respectively. The proposed algorithm can detect the crack with the minimum width of 3 mm and the minimum length of 10 cm in the depth image with a resolution of 1 984×2 000, and realize the target of detecting small cracks in the complex airport pavement scene. 2 tabs, 11 figs, 32 refs. © 2020, Editorial Department of Journal of Traffic and Transportation Engineering. All right reserved.