## 1 Introduction
The detection of bridge damage by manual inspection is currently the main method around the world. For example, mentioned in Section 1, bridge evaluations in the United States and China are still mainly conducted by manual inspection. The manual inspection usually first conducts professional and safety skills training for inspectors, and then searches and records the damage to the bridge through visual inspection on the bridge site, and then conducts a quantitative analysis after sorting. This method is currently used in most areas mainly because of its simplicity, However, there are high labor costs, which cause a large load on the inspection labor force, which is very time-consuming and inefficient. For bridges in bad weather and under dangerous conditions, the personal safety of the inspectors has been greatly challenged, and the detection accuracy is highly related to the professionalism of the inspectors. Therefore, at present, relevant researchers in defect detection are actively seeking alternative methods [20]. The non-destructive testing of bridge hardware equipment started relatively early. As early as 2007, American scientific researcher [PERSON] et al. [22] conducted a comparative analysis of three common bridge damage detection pieces of equipment: ground-penetrating radar, shock wave and infrared imaging. The results show that infrared thermal imaging is fast, easily affected by the environment, and has unreliable performance; the shock wave speed is slow and is greatly affected by the surface roughness. Ground penetrating radar is an ideal detection method, but the defect size must be greater than 0.5 inches to be detected, and even the lane needs to be closed for detection. The detection equipment mostly requires manual assistance, the equipment price is relatively high, and currently, hardware equipment is mostly used in the research of damage detection in the surface structure of bridges [23; 24; 25]; it is difficult to use on the side of bridges and under bridges overhead on land and sea, with considerable limitations, and it has difficulty meeting the needs of structural damage detection of viaducts, high-speed railway bridges and other land or sea overhead bridges in practical projects, so it is rarely used in practice. [PERSON] et al. [26] proposed the use of the Weighing Motion (WIM) system for damage identification of bridge structures. The WIM system is a system including sensors, computers and cameras installed in bridges. The installation often needs to be carried out through closed roads, and the WIN system is expensive, making it difficult to deploy large-scale applications for local bridges. At the same time, the biggest difficulty in bridge structural health inspection is how to accurately distinguish the impact of damage on the behavior of the structure from the impact of environmental and operational changes (EOV) [27]. Because of its dynamic characteristics, this method is sensitive to changes in the environment and operation, such as temperature, humidity, and sensor sensitivity. Therefore, it is more complicated in practical engineering applications and has difficulty meeting the requirements of low cost and high efficiency. Traditional image processing technology can use ordinary RGB images to conduct research on defect detection on a bridge structure's surface. The traditional image processing technology generally needs to set the features manually, based on features such as color [28], shape [29], texture [30] and others. The classification of the classifier is carried out to complete the identification of bridge damage. [PERSON] et al. [31] carried out detection research on the characterization of pavements through three traditional image processing techniques of double-layer threshold, morphological operation, and projection, and achieved ideal results under the condition of low complexity. However, the feature selection of this method is too simple, which makes it difficult to meet the complex background of practical engineering.