Abstract:

to address the problem of non-linear organization arrangement characteristics such as texture and unevenness on the surface of bridge steel structures， which increase the difficulty of corrosion point detection， a corrosion point detection algorithm based on synthetic aperture ultrasonic imaging technology was designed. Firstly， the multi element synthetic aperture focusing technology was adopted to obtain ultrasonic images of bridge steel structures. By focusing ultrasonic energy， the resolution and clarity of the ultrasonic images were improved， making it easier to identify and locate corrosion points. Secondly， using gray level co-occurrence matrix technology to capture the spatial relationships between pixels in the image， the arrangement features of the steel structure organization on the surface of the bridge were extracted. Finally， the Fisher discriminant criterion was used to remove features with low or redundant contribution to the detection， and the filtered features were input into the neural network to accurately detect corrosion points using the non-linear mapping ability of the neural network. The experimental results show that after applying the algorithm， the position and diameter distance of the corrosion points can be clearly observed. The diameter， inclination angle， and depth of the corrosion points detected by the algorithm are basically consistent with the actual values， indicating the effectiveness of the algorithm.

Key words: corrosion point detection, screening feature, synthetic aperture ultrasound imaging, neural networks, bridge steel structure, echo signal