Road pavement crack detection using deep learning with synthetic data
Ирина Канаева, Ju A Ivanova
Abstract
Abstract The improvement of road system quality is a critical task. The mechanism to address such important issue is close monitoring of road pavement condition. Traditional approach requires manual identification of damages. Taking into account considerable length of road system it is essential to create an effective automatic pavement defects detection tool. This approach will extremely reduce time for monitoring of current road state. In this paper global experience in solution of detection issues of road pavement’s distress is reviewed. The article includes information about the existing datasets of road defects, which are commonly used for detection and segmentation. The present work is based on deep learning approach with the use of synthetic generated training data for segmentation of cracks in driver-view image. The novelty of the approach lies in creating synthetic dataset for training state-of-the-art deep learning frameworks. The relevance of the research is emphasized by processing of wide-view images in which heterogeneous pixel intensity, complex crack topology, different illumination condition and complexity of background make the task challenging.