Detection of defective pharmaceutical capsules and its types of defect using image processing techniques

Real-time quality inspection of capsules manufacturing in pharmaceutical applications is an important issue from the point of view of industry productivity, competitiveness and quality aspect of the product. Pharmaceutical products are susceptible to several common flows like incorrect size or color, surface defect, missing, broken capsules. To guarantee every capsule is free of defects, each capsule must be inspected individually. In this paper we have compared different approaches of image processing for detection of defective capsule and presence of category of defects. All (sectorization of DFT transformed images for feature extraction, object counting, gray intensity distribution, color intensity distribution, GLCM, area counting and individual area calculation) methods proposed and compared in this paper are applied over database of 39 images of pharmaceutical capsule strips speeded over 3 different classes (single color, double color and Multi color). The experiment has been carried out to detect 5 different possible types of defects i.e. missing, broken, missing and broken, improper alignment and surface defect. The attempt has also been made to indicate the count of number of missing tablets/capsules in the strip. Overall Average and sum of area is calculated for the performance evaluation to detect the presence of defects. The range of differences obtained among the defective capsule images and defect free image has been analyzed to classify the category of defects present in the image. It has been observed that individual object area calculation, gray intensity density calculation and GLCM works better compared to all other approaches.