DAUNet: Deep Augmented Neural Network for Pavement Crack Segmentation

Abstract

Crack detection and measurement are essential tasks for maintaining and ensuring safety. Accurate crack detection is very challenging because of non-uniform intensity, poor continuity, and irregular patterns of cracks. The complexity of the background and variability in the data acquisition process also complicate the problem. Many approaches to crack detection have been proposed, but the accuracy of the detection leaves much to be desired. The aim of this study is to develop a practical crack detection method for real-time maintenance. We focus on a deep end-to-end and pixel-wise crack segmentation. We propose a lightweight U-Net-based network architecture with emphasis on the learning process. In order to verify the effectiveness of the proposed method, we conduct tests on publicly available pavement crack datasets and compare our model with state-of-the-art crack detection methods. Extensive experiments show that the proposed method effectively detects cracks in a complex environment, and achieves superior performance. The code and proposed model can be found in https://github.com/dvalex/daunet