A remote deflection detection method for long-span bridges using adaptive mask and high-resolution camera

A remote deflection detection method for long-span bridges using photogrammetry and computer vision is proposed. Through the proposed adaptive mask algorithm, the incorrect detection in the traditional photo-grammetry method based on feature point matching is avoided. In addition, an ultra-telephoto lens and a high -resolution camera are used to take a remote video of the bridge, which achieves high precision, high sampling frequency and non-contact deflection detection of the long-span bridge. Experiments show that the proposed method can effectively avoid incorrect detection, and the measurement accuracy can reach millimetre-level within 500 m under various meteorological conditions, which is higher than the measuring robot. It also has the characteristics of long detection distance, high detection accuracy, high sampling frequency, non-contact, automatic target search and low cost. This method not only meets the requirements of deflection detection of long-span bridges but also has great application prospects in other structural displacement detection tasks.

Automated summary

A remote deflection detection method for long-span bridges using photogrammetry and computer vision is proposed in this study. The method avoids incorrect detection through the use of an adaptive mask algorithm. It achieves high precision, high sampling frequency, and non-contact deflection detection of long-span bridges through the use of an ultra-telephoto lens and a high-resolution camera. The method has shown effective avoidance of incorrect detection and can reach millimeter-level measurement accuracy within a 500-meter range under various weather conditions, surpassing measuring robots. It also possesses long detection distance, high detection accuracy, high sampling frequency, non-contact, automatic target search, and low cost. This method not only meets the requirements of deflection detection for long-span bridges but also holds great potential for other structural displacement detection tasks.