Foreign Material Detection for Beverage Industry
Keywords:GLCM, Otsu, K-means, Foreign Materials Detection
Product quality inspection is required in industries for standardized products, which leads to the establishment of a quality inspection system. Nowadays, despite doing our best efforts on every beverage processor, sometimes foreign material might accidentally end up in the finished products. The problem is related to a manual inspection that can cause human error and product quality. Therefore, the main objective of this study is to design a detection system that can detect foreign materials on the beverages bottle. The proposed method used in this study consists of i) pre-processing, ii) image segmentation, iii) feature extraction, and iv) classification. Image processing algorithms in MATLAB may be used to identify foreign objects and contaminants in beverage bottles. The photograph must first go through the pre-processing stage. As a result, the segmentation procedure focuses on separating foreign materials from the contaminated region of the bottle. The Otsu method and the k means clustering method was used in this procedure. GLCM, histogram analysis, and quadratic distance were used to classify the product for feature extraction and classification. Both experimental and simulation findings are found to be identical for both parameters.