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Abstract

During the salmon can-filling process, a number of can-filling defects can result from the incorrect insertion of the salmon meat into the cans. These can-filling defects must be repaired before sealing the cans. Thus, in the existing industrial process, every can is manually inspected to identify the defective cans. This thesis details a research project on the use of machine vision for the inspection of filled cans of salmon. The types of can-filling defects were identified and defined through consultations with salmon canning quality assurance experts. Images of can-filling defects were acquired at a production facility. These images were examined and feature extraction algorithms were developed to extract the features necessary for the identification of two types of can-filling defects. Radial basis function networks and fuzzy logic methods for classifying the extracted features were developed. These classification methods are evaluated and compared. A research prototype was designed and constructed to evaluate the machine vision algorithms on-line.