# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
###### Abstract
In contrast, the evaluation of the quality of the test results in this paper uses other evaluation metrics, as described in Section 2.3 of the text. [PERSON] et al. [29] were the first to apply deep learning technology to building damage detection. In the experiment, 332 original images were used; 277 images were cropped and trained as a training set, and 55 images were used as a test set. Concrete cracks were detected using the CNN, and the resulting detection accuracy reached 98%, demonstrating the high application potential of deep learning in building damage detection. [PERSON] et al. [30] used CNN to detect damage in bridge structures. It can automatically extract features  
Figure 1: Detection of bridge cracks by UAV or ground photography. from low-level waveform signals and uses CNN to achieve high-precision identification of bridge damage locations with high detection accuracy, verifying the feasibility of deep learning technology for structural damage detection. [PERSON] et al. [31] proposed a new model-driven clustering method based on Faster R-CNN [32]. It is used to solve the problem of a low percentage of slender target area, thus improving the detection accuracy, effectively ensuring the integrity of the target area and avoiding leakage and false detection. [PERSON] et al. [33] employed the enhanced single-phase object detection algorithm from YOLOV3 [34], and the detection accuracy reached 93.02%, which was 1.37% higher than that of the original YOLOV3. The method had high real-time accuracy and could be used to detect different types of building damage. [PERSON] et al. [35] used a Sequential Convolutional Neural Network (SCNN), ResNet50 and Xception, respectively, to detect concrete cracks. The accuracy rates of SCNN, ResNet50 and Xception were 90.2%, 86.3% and 70%, respectively. [PERSON] et al. [36] proposed a multiscale feature fusion method of 3 SCNet+LBP+SLIC, where 3 SCNet performs crack detection, SLIC forms clusters of similar pixels and LBP finds the texture in the crack image, which improved the performance of CNN with accuracies of 99.47%, 99.75% and 99.69%, respectively, which could be applied to construct a real-time crack detection system. [PERSON] et al. [37] proposed a new method combining the Single Shot MultiBox Detector (SSD) and the eight-neighbourhood method to identify and detect bridge cracks. It used more than 300 crack images of the Beijing-Hangzhou Grand Canal Bridge and more than 1500 crack images collected as datasets and achieved good detection results for all four types of cracks, with an accuracy of more than 95% and a recall rate of more than 75%. The performance of this method in crack detection was significantly improved. Installation on the portable device provided an opportunity to extend its application to automatically detect cracks on concrete bridges. [PERSON] et al. [38] used deep CNNs to classify and recognise multicategory damage images of reinforced concrete bridges, using 150 images each of cracks, corrosion, defects and background as a dataset, and the correct recognition rate of damage images was 86%, with 82% of rebars exposed and 70% of concrete spalling. These studies show that in the field of engineering, the use of deep learning technology to detect and identify structural damage has broad research and application prospects, which will help to detect damage to buildings, bridges and other engineering structures more quickly and accurately and ensure the safety of people's lives and property. [PERSON] et al. [39] proposed a two-step strategy for detecting cracking in concrete bridges. First, the deep bridge crack classification (DBCC)-Net was used to implement the coarse extraction of crack locations in concrete bridges based on image slice classification. The complete crack shape of the concrete bridge was then extracted from the locations suggested by the SSNM. This method can quickly achieve crack detection and crack shape extraction in high-resolution images.