Enhancing Pavement Distress Detection Using a Morphological Constraints-Based Data Augmentation Method

Abstract

Pavement distress data in a single section usually presents a long-tailed distribution, with potholes, sealed cracks, and other distresses normally located at the tail. This distribution will seriously affect the performance and robustness of big data-driven deep learning detection models. Conventional data augmentation algorithms only expand the amount of data by image transformation and fail to enlarge the data diversity. Due to such a drawback, this paper proposes a novel two-stage pavement distress image augmentation pattern, in which a mask is generated randomly according to the geometric features of the distress in the first stage; and in the second stage, a distress-free pavement image with the fused mask is transformed into a pavement distress image. Furthermore, two convolutional networks, M-DCGAN and MDTMN, are designed to complete the generation task in two stages separately. In comparison with other generation algorithms, the quality and diversity of the generation results of proposed algorithms are better than other algorithms. In addition, distress detection tests are conducted which indicate that the expanded dataset can raise the IoU from 48.83% to 83.65% at maximum, and the augmented data by the proposed algorithm contributes more to the detection performance.