Pixel-level road crack detection in UAV remote sensing images based on ARD-Unet

Yuxi Gao, Hongbin Cao, Weiwei Cai, Guoxiong Zhou

Abstract: Crack is the main manifestation of road damage, and its further deterioration will affect road traffic. The timely detection of road cracks is of great significance for ensuring road safety. In this work, starting from UAV remote sensing images,a pixel-by-pixel crack detection method named ARD-Unet is proposed based on U-Net combined with Depth Separable Residual Block (DR-Block), Atrous Spatial Pyramid Fusion Attention Module (ASAM) and Receptive Field Block (RFB). We used UAV to construct a remote sensing road crack dataset containing 1046 high-quality images. The proposed method achieves 76.41 % MIOU and 74.24 % F1-Score on the self-made dataset. Finally, we combine ARD-Unet with UAV to build a road crack detection UAV IoT system, which has been tested in practical applications and achieved excellent performance. Experiments show that ARD-Unet is effective for road crack detection in remote sensing images.