A novel pavement crack detection approach using pre-selection based on transfer learning

Abstract

Most of the existing pavement image crack detection methods cannot effectively solve the noise problem caused by the complicated pavement textures and intensity inhomogeneity. In this paper, we propose a novel fully automatic crack detection approach by incorporating a pre-selection process. It starts by dividing images into small blocks and training a deep convolutional neural network to screen out the non-crack regions in a pavement image which usually cause lots of noise and errors when performing crack detection; then an efficient thresholding method based on linear regression is applied to the crack-block regions to find the possible crack pixels; at last, tensor voting-based curve detection is employed to fill the gaps between crack fragments and produce the continuous crack curves. We validate the approach on a dataset of 600 (2000 × 4000-pixel) pavement images. The experimental results demonstrate that, with pre-selection, the proposed detection approach achieves very good performance (recall = 0.947, and precision = 0.846).