ERNet: A Rapid Road Crack Detection Method Using Low-Altitude UAV Remote Sensing Images

Abstract

The rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently, this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can provide images with a centimeter-level or even subcentimeter-level ground resolution, which provides a new, efficient, and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we proposed a real-time edge reconstruction crack detection network (ERNet) that adopted multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and the background. To capture global dependencies across spatial and channel levels, we proposed an efficient bilateral decomposed convolutional attention module (BDAM) that combined depth-separable convolution and dilated convolution to capture global dependencies across the spatial and channel levels. To enhance the accuracy of crack detection, we used a coordinate-based fusion module that integrated spatial, semantic, and edge reconstruction information. In addition, we proposed an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. The experimental results demonstrated that our network achieved the best balance between accuracy and inference speed compared to six established models.