Non-contact measurement technology based on computer vision has been recognized as a critical approach in bridge lightweight monitoring due to its low cost and strong environmental adaptability. To address the sub-millimeter accuracy and real-time requirements of bridge displacement monitoring, this study proposes a visual monitoring method that integrates a connected-domain segmentation matching algorithm with an automatic binarization threshold adjustment mechanism. This combination significantly improves adaptability and robustness under complex lighting conditions. Moreover, the method introduces the SRCNN (Super-Resolution Convolutional Neural Network) as a lightweight super-resolution module, the method achieves a better balance between computational efficiency and measurement precision. The proposed method was validated through model testing and successfully applied to real-bridge displacement monitoring and structural damping ratio identification. These findings demonstrate the practical potential of the method as a reliable reference for static and dynamic performance evaluation and condition assessment of bridges.
Sun et al. (Sat,) studied this question.
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