Superpixel level Gabor identification method for defects in anti lateral force brackets of prefabricated steel structure buildings

In order to improve the safety and reliability of prefabricated steel structure buildings, a superpixel level Gabor identification method for the defects of anti-lateral force brackets in prefabricated steel structure buildings was designed. The images of defects in the anti-lateral force bracket were normalized. The dataset was expanded through image rotation, cropping transformation, and mirror flipping processing. A multi-task learning based image superpixel segmentation method consisting of multimodal feature extraction mechanism, robust pixel similarity evaluation, and pixel to superpixel soft mapping strategy was used to implement anti lateral force stent defect image superpixel segmentation. For generating superpixels, their Gabor filtering was implemented through a two-dimensional Gabor filter to extract superpixel features. Based on the extracted Gabor local phase features and Gabor local directional features, SVR was used to identify defects in anti-lateral force brackets. The experimental test results showed that the design method could identify various lateral force resistant bracket defects. For all sizes of defects, the false positive rate of the design method was low, and the false positive rate for identifying fine defects was only 0.025.