Non-uniform illumination image enhancement for surface damage detection of wind turbine blades. (1st May 2022) - Peng, Yeping; Wang, Weijiang; Tang, Zhen; Cao, Guangzhong; Zhou, Shengxi
Abstract: Vision-based monitoring technology has been exploited for the surface damage detection of wind turbine blades (WTBs). However, the image quality is often significantly influenced by environmental illumination conditions, imposing difficulties for obtaining high detection accuracy for large-scale WTB surfaces. To improve the image quality and guarantee reliable damage detection on WTB surfaces, this study presents an image processing method for enhancing the images captured under non-uniform illumination conditions. First, cartoon and texture maps of the WTB images are constructed by cartoon texture decomposition. Second, an illumination model is established on the cartoon map from the Gaussian scale-space, to remove the non-uniform illumination. Third, the WTB images are enhanced by utilizing a multi-directional Gabor transformation to increase the contrast between the surface damage and image background. Finally, the WTB surface damages are detected using a gradient threshold segmentation method. The experimental results indicate that the damage detection accuracy of the WTB surfaces is significantly improved by using image enhancement. The F-measure and the intersection-over-union values of the damage detection are increased by 28.05% and 41.61%, respectively, relative to those detected from the input images. Therefore, this vision-based detection method for WTB surface damage inspection under non-uniform illumination has potential application values in practice. Highlights: This work contributes to the wind turbine blade (WTB) surface damage detection under non-uniform illumination conditions. The WTB images are decomposed to the cartoon and texture maps for enhancement processing. The image contrast is increased based on the multi-directional Gabor transformation to improve the damage detection accuracy. The proposed method can be developed for the product surface defect detection especially when the image samples are insufficient.