Robust Principal Component Analysis for Aluminum Foil Surface Defects Detection

 In order to accurately detect the defects on the aluminum surface, including perforation, stains, shine marks and scratches, etc., a method of detection of aluminum foil images surface defects based on low-rank sparse decomposition is proposed. The probability of aluminum foil surface defects occurring in the production process is low and the defects typically constitute a small area proportion on a foil image. In other words, a linear relationship may be presumed between a foil image and the background, which may both be roughly deemed to under the same low-rank subspace, and a further presumption is that surface defects are approximately sparse. The observation data matrix consisting of a foil image sequence is put to a low-rank sparse decomposition using RPCA(Robust Principal Component Analysis) algorithm, resulting in low-rank background images and sparse defective images. A low-rank sparse decomposition test is performed on image sequences consisting respectively of single foil images and multiple foil images, and the validity of the proposed technique is demonstrated in an applicable detection process of foil image defects. Experimental results showed that the proposed algorithm detect defects were clear and complete, processing of an image with a size of 880×540 takes no more than 0.7 second on average, and it’s able to realize real-time detections. Algorithms presented in this paper are featured with rather favorable expansibility, and it can be easily applied into surface defect detections for other objects.