Abstract: Abstract: Aiming at the problem of surface defect detection of wind turbine blades, a method based on robust principal component analysis (RPCA) and visual saliency is proposed. Based on RPCA, the method adds the noise term and Laplacian regularization term to facilitate the segmentation of defect images, that is, suppressing Gaussian noise and uneven illumination by F-norm regularization term, and constraining the spatial relationship of pixels with Laplacian regularization term which can preserve invariance and the local consistency among the spatially adjacent sub-regions with similar saliency values in a saliency map. Firstly, superpixel segmentation and feature extraction are performed on the surface image of input wind turbine blades to obtain the feature matrix of the image. Then, the sparse matrix is obtained by the improved PRCA, and the saliency map of the defect region is calculated according to the sparse matrix and visual saliency method. Finally, the saliency map is optimized and the adaptive threshold segmentation algorithm is used to detect defects. Through experimental simulation, the experimental results are qualitatively and quantitatively analyzed, which indicates that the proposed method has high detection accuracy.

Key words: Keywords: RPCA, visual saliency, defect detection, wind turbine blades