# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
## 4 Discussion  
The instance segmentation algorithm represented by Mask R-CNN [56] could perform the dual tasks of object detection and semantic segmentation simultaneously. However, as the FCN segmentation algorithm was applied to the R-CNN algorithm, its computational efficiency was low. For the object with complex crack contour information in particular, its crack segmentation accuracy was far from the practical engineering requirements. Inspired by the idea of Mask R-CNN, [PERSON] et al. [39] proposed a two-stage crack-detecting scheme for concrete bridges. First, the DBCC-Net model was used to realise the coarse detection of cracks in concrete bridges based on image slice classification. The complete crack shape of the concrete bridge is then extracted from the locations suggested by the SSNM. [PERSON] et al. [57] proposed a multitask integrated crack detection algorithm. First, the crack feature was extracted using the object detection algorithm and the feature information was passed to the classification, regression and segmentation network modules, respectively. Then, using the results of classification and localisation, a crack object detection localisation map was generated, and this localisation map was calibrated against the concatenated domain of the semantic segmentation results. By calibrating the submodule, only the connected domains containing cracks were retained. The aforementioned methods enhance the performance of crack segmentation to some extent, but it is still difficult to obtain high-performance crack detection in the presence of factors such as the complicated background, the small percentage of cracks themselves and the variable topology.  
In this paper, a coarse-fine combined bridge crack detection method of \"double detection + single segmentation\" based on deep learning was proposed. The initial detection of the crack image was first performed by a deep learning-based initial crack detection network, and a set of rectangular boxes containing cracks was obtained. Then, a deep learning-based fine crack detection network was used within each rectangular box to fine-detect and locate the initially detected crack areas to obtain more accurate crack locations.  
Finally, image semantic segmentation technology was used for fine segmentation of the detected cracks to complete the fine detection of the bridge cracks. Among them, the coarse detection stage could exclude the influence of the complex background of the bridge cracks on the crack detection results, and it could quickly screen out the areas that might contain cracks, reduce the computational effort of the subsequent fine detection and improve the effectiveness of crack detection. In the fine detection stage, the coarse detection results of bridge cracks could be further accurately identified, false detection could be eliminated and the detection accuracy of bridge cracks could be improved. In the fine segmentation stage of bridge cracks, the features of the detected cracks could be accurately segmented and extracted, and the highly accurate contour information of the cracks could be obtained, providing accurate data for the subsequent quantification of the bridge crack width.