Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning

Abstract

This work focuses on solving two challenging problems in pavement crack detection: (1) noises caused by complicated pavement textures and intensity inhomogeneity cannot be removed effectively, which makes crack extraction difficult; and (2) sealed cracks and cracks with similar intensity and width cannot be separated correctly, which makes data analysis and budgeting inaccurate. Here, a unified crack and sealed crack detection approach is proposed that can detect and separate both cracks and sealed cracks under the same framework. It trains a deep convolutional neural network to preclassify a pavement image into crack, sealed crack, and background regions. A blockwise thresholding method is developed to segment the crack/sealed crack pixels efficiently and effectively. Finally, tensor voting–based curve detection is applied to extract the crack/sealed crack. The proposed approach is validated using 800 images (each $\mathrm{2,000}\times \mathrm{4,000}\mathrm{pixels}$ ); the experimental results demonstrate that this approach accurately distinguishes cracks from sealed cracks and achieves very good detection performance ($\mathrm{recall}=0.951$ ; $\mathrm{precision}=0.847$ ).