Pavement crack measurement based on aerial 3D reconstruction and learning-based segmentation method

Abstract

Rapid inspection of urban road cracks is vital to maintain traffic smoothness and ensure traffic safety. A rapid pavement crack inspection method uses low-altitude aerial images captured by an unmanned aerial system (UAS) and deep-learning aided 3D reconstruction, and a learning-based object segmentation algorithm is proposed to measure road cracks automatically. The contributions include: (a) An efficient 3D reconstruction method for low-altitude aerial images captured by a UAS is proposed, which applies an instance segmentation network to segment road targets from raw images with complex backgrounds first and then performs structure from motion to reconstruct a large-scale road orthophoto from a large number of aerial images. (b) To detect cracks from the reconstructed large-size road orthophoto, a sliding window algorithm and U-Net model optimized with a transformer structure are used to automatically identify and segment the cracks from the orthophoto at the pixel level. Then, a connected domain feature analysis method is used to measure the road crack length. The proposed method is applied to detection of road cracks in a 1.5 km2 area of a city. The results show that the proposed method can effectively and accurately detect cracks and measure the length of cracks in the 4-km-long road, which proves the practicality of the proposed method.