## 1 Introduction
[PERSON] [32] adjusted the gray level of the image, then used the Otsu method to preprocess the image, and then cooperated with the shaping algorithm to detect damage. The experimental results show that it needs to be combined with other shapes to perform accurate detection, and the detection method is complex and has difficulty meeting the requirements of real-time performance. In recent years, with the accelerated development of computer hardware facilities, the computing power of GPU has been greatly improved, which has created a material basis for computer technology based on deep learning theory. On top of this, computer vision technology based on deep learning theory has been developed rapidly. Compared with the target detection of traditional image processing methods, target detection based on deep learning theory has a good performance in terms of generalization and robustness [33; 34]. Generally, we divide the target detection algorithm based on deep learning theory into a two-stage detection algorithm and a single-stage detection algorithm. The two-stage detection algorithm is the pioneer of the target detection algorithm based on deep learning. The representative two-stage detection algorithms include Msk R-CNN [35], Fast RCNN [36] and Faster RCNN [37], and other network algorithms. The algorithm principle is generally to first generate a candidate frame and then use a convolutional neural network to extract features and classify them. Single-stage detection algorithms usually complete feature extraction, classification, and prediction in one step, with the advantages of small model size and fast speed. Typical single-stage detection algorithms include the SSD algorithm [38] and the YOLO series algorithm [39] etc. Their accuracy is lower than that of the usual two-stage algorithms, but with the introduction of the sample imbalance function, the performance of the single-stage object detection algorithm is also improving and even surpassing the two-stage detection algorithm [40]. However, at present, most of the bridge defect detection based on deep learning only focuses on the detection of damage to bridge pavements. For example, [PERSON] et al. [41] realized defect detection of a concrete bridge pavement by improving the classical convolutional neural network and visual geometry group network-16 (VGG-16). The substructure of the bridge, as its load-bearing structure, is often more critical to the maintenance of the bridge. [PERSON] et al. [42] realized the detection of bridge damage by improving Faster RCNN and found that its accuracy is higher through experiments. However, the Faster RCNN network model is large, the flexibility is poor, and the detection speed is also slow, which makes it difficult to meet the real-time and flexibility requirements of practical engineering. [PERSON] et al. [43] used deep learning theory combined with infrared and lidar to achieve end-to-end detection of regional defects in bridges, but the detection cost is relatively high. [PERSON], [PERSON] et al. [44] improved the YOLOv3 network model to detect the number of cracks, and the detection speed has been greatly improved compared with the two-stage detection algorithm. However, the model only performs single classification, and its generalization performance is difficult to determine for practical projects. [PERSON] et al. [45] conducted a comparative analysis of YOLO, SSD, HOG with SVM and Faster R-CNN network models for the detection of concrete defects. The results show that the YOLOv3 model of the YOLO network algorithm series is the most robust for defect detection and has a reliable application prospect. [PERSON] et al. [46] proposed to deploy the YOLO algorithm into an embeddable device to detect pavement defects, but the network structure of the YOLOv3 network model is too large and has poor flexibility, which cannot meet the conditions of general hardware. [PERSON] [47] conducted experimental analysis on three algorithms, YOLOv5, YOLOR and Faster R-CNN, for road surface defect detection.