# Bridge Displacements Monitoring Method Based on Pixel Sequence
## 1 Introduction
This system is non-contact, adaptable to various environments, and capable of working in low-light conditions, but it is limited to in-plane displacement and constrained by UAV camera frame rates. The method also faces challenges with setup and measuring out-of-plane displacements. [PERSON] et al. [13] focused on cable tension estimation for the Dubrovnik cable-stayed bridge using handheld camera video and image analysis techniques. The approach provides stable displacement measurements and tension estimates comparable to design values but struggles with small-amplitude or high-frequency vibrations. [PERSON] et al., presented a reliable UAV-based method for estimating cable tension using template matching and optical flow algorithms. This method has shown high accuracy in both lab and field tests, with minimal deviation from reference values. However, it is limited by UAV battery life, high computational costs, and environmental instability. Future work for both methods could explore integration with IMUs for real-time monitoring and improve small cable vibration detection, enhancing their applicability for SHM in cable-stayed bridges. [PERSON] et al., discussed the displacement monitoring technology based on machine vision, highlighting its advantages and exploring methods for synchronous multi-point displacement detection. They also analyzed the current limitations of machine vision technology and suggested future research directions. [PERSON] et al., proposed a method utilizing Scale-Invariant Feature Transform (SIFT) for extracting structural feature points, facilitating the study of Full-Field Displacement Vectors (FFDVs) to achieve multi-point displacement monitoring across an entire structure. Meanwhile, [PERSON] et al., introduced a multi-point displacement measurement method for bridges using a low-cost camera, with the accuracy of the method validated through experimental tests. It can be seen that compared to traditional contact sensor displacement monitoring, non-contact displacement monitoring is easier to install and does not require contact with the structure. Typically, bridge deformation monitoring systems rely on sensorsand other equipment. For small and medium-sized bridge monitoring projects that use sensors, the costs, including sensors, labor, installation, and others, can easily reach millions of CNY. In contrast, machine vision-based monitoring methods only require a visual acquisition system, targets, and data storage and processing equipment. This results in a cost reduction of 50-70% compared to sensor-based systems [14; 15]. It can achieve wide-area monitoring coverage and, by avoiding the limitations of high-precision sensors, can be applied in various environments. Additionally, it does not require frequent replacement or maintenance of sensors, is cost-effective, and does not interfere with the normal operation of bridges. This paper proposes a machine vision system for monitoring displacement in standard bridge structures. This system mainly consists of a high-definition camera and a data processing unit, with the latter comprising only a regular PC and MATLAB R2023b software. Compared to other methods, this system offers advantages such as simple usage conditions, low cost (requiring only a single camera setup), and low learning curve (due to simple algorithms). It is referred to as the DoG Structural Displacement Recognition System (DoG stands for Difference of Gaussians). During the validation process, a theoretical and practical comparison method was employed, using the DoG algorithm to monitor a simulated structure and comparing the results with displacement data to verify the algorithm's accuracy and applicability.