An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement

Abstract

Discovering and assessing cracks is widely thought to be critical for maintaining the healthy conditions of asphalt pavement. Unfortunately, the inspection of pavement for cracks is not only labor-intensive, time-consuming, inefficient, and costly, but it is also unable to detect and quantify cracks accurately at the pixel level. To address this problem, we propose an integrated approach based on the convolutional neural network DeepLabv3+ for crack detection, as well as a crack quantification algorithm for crack quantification at the pixel level. The quantification algorithm is used to evaluate five important indicators: crack length, mean width, maximum width, area, and ratio. To fully verify the performance of DeepLabv3+, 50 images were studied; the best image showed a mean intersection of union (MIoU) of 0.8342. For testing, 80 new images (including both asphalt pavement images and concrete pavement images) were used. DeepLabv3+ was found to be reliable and widely applicable for crack detection, and it demonstrated an MIoU of 0.7331. Of the various quantitative indicators, the crack length had the lowest relative error rate of the predicted values and therefore had the highest accuracy (its relative error rate ranged from −25.93% to 14.11%). We also compared our system with four state-of-the-art methods. The results showed our integrated approach to be more effective and more accurate in both the detection and quantification of cracks. The integrated approach could potentially serve as the basis of an automated, cost-effective pavement-condition assessment scheme for the operation and maintenance of pavement.