## 1 Introduction  
In recent years, with the deepening of the industrialization process in various countries in the world, the problem of aging infrastructure brought about by industrialization has become increasingly prominent, and the timely detection and improvement of aging infrastructure have become a focus of global attention. According to the American Society of Civil Engineers' 2021 Infrastructure Transcript Report [1], there are more than 617,000 bridges in the United States, 42% of which are at least 50 years old, and about 7.5% of the bridges in the country are already defective to varying degrees. In China, according to official statistics, by the end of 2014, the number of highway bridges had reached 750,000 [2]. With the increase in the number, the problems caused by bridge structural damage have become more and more serious. From 2000 to 2014, 179 bridges collapsed in China [3], causing irreversible losses to the national economy and national life safety. Therefore, bridge safety is indispensable in the construction of our modern society and plays a very important role in the travel of human traffic and the connection of regional economies [4]. However, the aging problem of bridges caused by factors such as repetitive vehicle loading, temperature difference, corrosion, and human damage is becoming more and more prominent. If the bridge maintenance department can detect the potential problems of the bridge promptly and take measures to fix them, the loss of economy and life can be well reduced. For the normal operation of bridges, regional governments usually formulate strict laws to restrict the bridge inspection departments. For example, in North America, the inspection of bridges uses strict standards (The Ontario Structure Inspection Manual) to conduct routine inspections every two years by manual means to ensure the daily quality of bridges [5]. In the United States, the government also conducts manual and periodic bridge damage inspections according to the strict National Bridge Inspection Standard (NBIS) [6]. In Europe, the quality of bridges is assessed by manual inspections using the standards set by TU1406 [7]. In order to meet the requirements of laws and regulations and ensure the safety of people's transportation and the needs of the national economy, the bridge maintenance departments in various regions need to consume a lot of manpower and material resources to carry out routine inspections of bridge maintenance. The inspection of bridge quality is not only required for daily bridge maintenance. During the life cycle of the bridge, it may suffer from various unpredictable hazards, and it is necessary to carry out a performance evaluation and prediction of the bridge in each period through regular inspection of the bridge [8]. The United States conducts visual inspections of the physical condition of bridge decks, superstructures, and substructures by using the National Bridge Inventory (NBI) Condition Rating System [9], quantifying the results of inspections to assess bridge life cycle reliability [10], life cycle cost [11], risk [12], sustainability [13], and utility [14; 15]. Through the analysis of the above indicators, decision-makers can make optimal decisions on the design, construction, maintenance, repair, and management of the bridge's life cycle under expected conditions to maximize the benefits of the bridge life cycle. Therefore, the detailed condition detection and evaluation of bridges is an important basis upon which to achieve optimal decision-making. However, the traditional inspection method mainly relies on manual visual inspection, which not only causes huge inspection cost, but also causes problems of low efficiency and inconsistent standards (affected by subjective factors) [16; 17]. At present, the working methods for bridge defect detection can be roughly divided into four categories: manual inspection [18], hardware equipment participating in detection [19], traditional image processing methods [20], and deep learning-based computer vision technology [21].