# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
## 3 Coarse-Fine Combined \"Double Detection + Single Segmentation\" Fine Crack Detection
### Example of Fine Crack Detection  
Based on the above experimental results, the Faster R-CNN model and the YOLOV5(x) model were used for initial and fine crack detection in bridge cases, respectively. The U-Net model was used for fine crack segmentation by combining shallow and deep features in a meaningful way. The technical process and results are shown in Figure 10. Among them, \"F-F-U\" means that Faster R-CNN is used for initial and fine crack detection, respectively, and U-Net is used for fine crack segmentation. Similarly, \"F-V5-U\" means that Faster R-CNN is used for initial crack detection, YOLOV5(x) for fine crack detection and U-Net for fine crack segmentation. \"V5-F-U\" means that YOLO V5(x) is used for initial crack detection, Faster R-CNN for fine crack detection and U-Net for fine crack segmentation. Figure 9: Technical route to fine crack detection in concrete bridges. From the experimental results in Figure 10a, the following could be seen: (1) The combined F-F-U model is used to detect cracks in bridges with high-resolution and complex backgrounds. The Faster R-CNN model locates three crack regions in the image in the initial detection stage and further detects the initially located crack region in the fine detection stage to obtain a series of smaller rectangular regions containing crack information. Through the above two detection steps, the interfering factors such as the fence and road surface on the image are completely eliminated, and the excess background information around the crack is further reduced, which greatly reduces the difficulty of the U-Net model in the fine segmentation stage. However, due to the lack of detection in the fine detection stage, the cracks are not completely eliminated, and the excess background information around the crack is further reduced, which greatly reduces the difficulty of the U-Net model in the fine segmentation stage. However, due to the lack of detection in the fine detection stage, the cracks are not completely eliminated, and the excess background information around the crack is further reduced, which greatly reduces the difficulty of the U-Net model in the fine segmentation stage. Figure 10: Fine detection results of bridge cracks: (**a**) breakdown diagram of crack detection; (**b**) the location of the bridge crack detection results (V5-V5-U). stage, the final crack detection results are not complete. (2) The combined F-V5-U mode was adopted to detect the bridge crack image under a high-resolution and complex background. Compared with the Faster R-CNN model, the YOLOV5(x) model only filtered and divided the background into both sides of the entire crack in the fine detection stage to ensure the integrity of the crack but still caused a lack of detection. It also misdetected the shadow under the bridge as a crack. However, the U-Net model can effectively discriminate the false detection information in the fine segmentation stage and finally obtain the crack profile information with high accuracy. (3) The combined V5-F-U mode is used to detect the bridge crack images under high-resolution and complex backgrounds. The YOLOV5(x) model has better anti-interference for the complex background information in the image and locates four crack regions in the image at the initial detection stage. However, the Faster R-CNN model has both false detections and missing detections at the fine detection stage, resulting in poor connectivity of the crack detection results obtained by fine segmentation. (4) The combined V5-V5-U model is used to detect the bridge crack images in high-resolution and complex backgrounds. The YOLOV5(x) model also has better anti-interference for the complex background formation in the image and locates four crack regions in the image at the initial detection stage.