Rapid inspection of urban road cracks is vital to maintain traffic smoothness and ensure traffic safety. A rapid pavement crack inspection method using low-altitude aerial images captured by the unmanned aerial system (UAS) and deep-learning aided 3D reconstruction method, learning-based object segmentation algorithm is proposed to measure road cracks automatically. The contributions include: (1) An efficient 3D reconstruction method for low-altitude aerial images captured by UAS is proposed, which applies an instance segmentation network to segment road targets from raw images with complex backgrounds first and then perform structure from motion (SFM) to reconstruct a large-scale road orthophoto from a large number of aerial images. (2) To detect cracks from the reconstructed large-size road orthophoto, the sliding window algorithm and U-Net model optimized with transformer structure are used to automatically identify and segment the cracks from the orthophoto at a pixel level. Then the connected domain feature analysis method is used to measure the road crack length. The proposed method is applied to detect road cracks in a 1.5km2 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 6.2km-long road, which proves the practicality of the proposed method.