In light of the challenges posed by intricate algorithms, subpar recognition accuracy, and prolonged recognition duration in current machine vision for bridge structure monitoring, this paper presents an innovative method for recognizing and extracting structural edges based on the Gaussian difference method. Initially, grayscale processing enhances the image’s information content. Subsequently, a Region of Interest (ROI) is identified to streamline further processing steps. Following this, Gaussian check images at different scales are processed, capitalizing on the observation that edges show reduced correspondence to the Gaussian kernel. Then, the structure image’s edges are derived using the difference algorithm. Lastly, employing the scale factor, the algorithm translates the detected edge displacement within the image into the precise physical displacement of the structure. This method enables continuous monitoring of the structure and facilitates the assessment of its safety status. The experimental results affirm that the proposed algorithm adeptly identifies and extracts the structural edge’s geometric characteristics with precision. Furthermore, the displacement information derived from the scale factor closely aligns with the actual displacement, validating the algorithm’s effectiveness.