Aiming at the problem that traditional bridge crack detection algorithms have poor antinoise ability and difficulty processing crack images with complex backgrounds, and the conventional deep learning image segmentation algorithm has low spatial accuracy, a bridge crack detection method based on multi-resolution and high spatial accuracy is proposed. First, the unmanned aerial vehicle is used to collect bridge images. The bridge crack dataset is obtained through image enhancement processing. Then, the parallel connection is used to connect multi-resolution subnets and repeated multi-scale fusions, so that the detection model maintains high-resolution representations throughout the process, while performing repeated multiscale fusion using low-resolution representations of the same depth and similar level. This is to improve the high-resolution representation so that high-resolution representation also exhibits strong high-level semantic features. Finally, the proposed algorithm is trained and verified on the dataset. The results show that all the segmentation indexes of the proposed algorithm are significantly improved, the accuracy of crack detection is as high as 93.8%, and the average interaction ratio reaches 85.48%.