# Bridge Displacements Monitoring Method Based on Pixel Sequence
## 1 Introduction  
The Structural Health Monitoring (SHM) system, as a crucial part of the bridge's lifecycle, plays an essential role in ensuring the safety, reliability, and maintainability of bridges [1]. During a bridge's service life, it is subjected to various loads, such as seismic forces from the natural environment and common wind loads. These loads cause mechanical responses in the structure, which the SHM system monitors to evaluate the structural mechanical performance. The most direct expression of the structural mechanical response is displacement, which varies with different input loads. Displacement, as a fundamental quantity, can reflect the bridge's vibration, stiffness, and other mechanical signals. Therefore, accurately extracting the bridge's structural displacement is key to achieving a highly accurate bridge monitoring system. Traditional displacement monitoring technologies primarily rely on sensors, such as strain sensors, linear displacement sensors, dial gauge sensors, laser displacement sensors, and accelerometers [2, 3]. Although sensors can provide accurate monitoring results, the data transmission from sensors heavily relies on data cables and signal wires. During long-distance signal transmission [4], the installation and maintenance of these cables is a significant challenge that cannot be ignored [5]. Furthermore, they can only monitor displacements at certain points in the structure, and the transmission of data in a high-speed and accurate way is also a key issue. Hence, there is an urgent need for more advanced methods to upgrade the SHM systems for bridges. SHM systems utilizingGlobal Navigation Satellite System (GNSS), laser Doppler vibrometers, and radar systems have been proven reliable and useful [6], but these systems highly depend on power and communication systems. With the rapid development of computer technology, image-processing technology has also emerged. By analyzing optical signals in images using computers, machine vision-based bridge structure displacement monitoring technology has gradually matured. [PERSON] et al. [7] used deep learning and computer vision methods, employing deep multilayer perceptrons to identify the influence surface of bridge structures and study the traffic load and state evaluation of the structure. Experiments demonstrated the framework's high accuracy and robustness. [PERSON] et al. [8] used machine vision technology to identify vehicle trajectories, combined with millimeter-wave radar to monitor the structure's displacement response, and calculated the vehicle's axle load using the structural displacement influence line, achieving vehicle-bridge coupling analysis. [PERSON] et al. [9] used UAV technology to monitor the structural health of bridges and extract structural displacement. The use of UAV monitoring has the advantages of low cost, flexibility, and high accuracy in extracting bridge structural displacement. [PERSON] et al. [10] researched vision-based displacement monitoring (VDM) and developed a 2D VDM method to achieve VDM in three-dimensional space using two cameras. [PERSON], [PERSON], [PERSON] et al. [11] proposed a UAV-based structural monitoring method to measure sway displacement, addressing the challenges of traditional SHM systems by bypassing fixed camera positions and improving measurement accuracy through advanced algorithms. However, the method faces high computational demands, limited UAV flight time, and sensitivity to wind. Similarly, [PERSON] et al., introduced a UAV-based non-contact cable force estimation method using line segment matching to address displacement calculation issues. Although it provides a cost-effective and robust solution for dynamic displacement, it is suitable only for cables with large vibrations under low-wind conditions, and UAV motion can affect the results. [PERSON] et al. [12] developed a vision-based method for measuring structural displacement with UAVs and lasers, using black-and-white markers and laser projection.