# The Current Development of Structural Health Monitoring for Bridges: A Review
## 2 Monitoring Technology
### Computer Vision-Based Methods  
For decades, computer vision-based methods and image-processing technology have been widely applied and studied in many fields [52; 53; 54; 55; 56; 57; 58; 59; 60]. Due to its advantages of non-contact, long distance, low cost, and high resolution, this new measurement technology has been applied to the study of SHM by many scholars. In 2017, [PERSON] and [PERSON] [61; 62] reported a new framework for SHM systems. They proposed a method for measuring displacement and vibration based on non-target computer vision, and then developed a camera calibration method to address the unavailability of traditional calibration standards. The effectiveness of the proposed method was verified by comparing the measurement results with the traditional sensors in a four-span bridge model and stadium structure. In2019, [PERSON] et al. [63] combined computer vision with deep learning (DL) to detect anomalies in monitoring data. When the data was visualized, the signal was converted into an image vector to input the deep neural network. In 2022, [PERSON] et al. [64] studied displacement estimation techniques at different sampling frequencies. They combined a hybrid computer vision algorithm with a Kalman filter and used a new calibration algorithm to estimate the high-sampling displacement. The error of the estimated displacement in the experimental verification was less than 1.5 mm. This technique has practical application potential in long-term structural displacement monitoring. In 2023, [PERSON] et al. [65] proposed an automated method for identifying defects in RC bridges using computer vision. They first trained a neural network using images of defective areas from existing RC bridges. Additionally, they introduced the Class Activation Maps (CAMs) method in Explainable Artificial Intelligence (XAI) technology to explain the recognition results of the deep learning method and achieve the highlighting of specific defect types. Recently, some researchers have begun to integrate vehicle load measurements into SHM systems using computer vision technology. [PERSON] and [PERSON] [66] developed an identification framework with a new damage indicator using computer vision-based vehicle load modeling and image-based structural identification. The damage recognition capability of the framework was verified on a laboratory model. [PERSON] et al. [67] combined the influence line theory with computer vision technology in their study. They proposed a traffic sensing method to collect traffic load information. The effectiveness and accuracy of this method were verified in the system analysis of a continuous box-girder bridge. This method is expected to be widely used in bridge weigh-in-motion (WIM) systems. [PERSON] et al. [68] studied the method for accurately associating traffic load with bridge response. In their proposed framework, a computer vision approach based on DL was used to accurately identify trucks from field images. The framework was applied to a 20-mile highway corridor to verify the correlation between bridge response peak and measured truck weight, based on one year's measured data. In practice, the existing identification methods usually require prior information of the road to locate the traffic load, which is tricky in some cases. [PERSON] et al. [69] studied the position relationship between the camera and passing vehicles. They proposed a recognition method that computes the spatiotemporal information of vehicles by mathematically processing camera locations. The reliability of this method was verified by laboratory tests and field measurements. [PERSON] et al. [70] improved the existing traffic load monitoring (TLM) technology for the entire bridge deck. They developed a dual-target detection model based on DL to identify vehicle features captured by cameras. In addition, they proposed an optical geometry model to accurately estimate vehicle position.