Classification of damages in composite images using Zernike moments and support vector machines

Abstract

In this work, the strength of the composite material is tested and the damages are classified using supervised method. The image is obtained from the front and rear sides of the composite material after applying 5 mm, 6 mm and 7 mm impingement. Initially, the images are filtered using anisotropic diffusion filter. Global and local damages in the structures are segmented using Fuzzy C-Means (FCM) clustering method. Geometrical features and Zernike Moments (ZM) are calculated from the delineated regions. The performance of the features is tested using Support Vector Machine classifier. Results show that the FCM with three and four cluster centres is able to segment the global and local damages respectively. The global damages due to different impinges are classified better compared to the local damages. The global damages in the rear side are able to classify better compared to the front side in both geometrical and ZM features. In the case of local damages, the rear side is able to classify better in 5 mm–6 mm and front side in 6 mm–7 mm. It is concluded that the features obtained from the ZM gives better accuracy in both global and local damages compared to the geometrical features. The image based analysis carried out on this work is able to classify the impairment in composite materials; this framework can be used in the industrial applications for the quantification of damages.