Machine Vision Systems for Industrial Quality Inspection

Abstract

The recent progress in imaging sensors, artificial intelligence, real-time processing hardware has led to a change in industrial quality inspection, where machine vision systems have become a key idea. The conventional manual inspection processes can be quite tedious, irregular and inapplicable to high production throughput. Machine vision systems offer fully automated, objective and repeatable inspection systems with benefits that improve the quality of the products, minimizes the cost of operations and acquires the industry with high standards. The paper is a thorough examination of machine vision systems used in industrial quality inspection especially in the areas of system architecture, image acquisition, preprocessing, feature extraction, defect detection and decision making processes. The incorporation of classical computer vision methodology with the latest deep learning systems like convolutional neural networks has achieved a high rate of quality defect detection, high resilience, and scalability in various manufacturing industries such as the automobile, electronics, pharmaceutical, and food processing industries. Moreover, the paper discusses the issues of inconsistency in lighting, real-time, imbalance in the data set, and system integration with industrial automation systems. Recent studies experimental tests have indicated that machine vision based inspection systems have the capability of having accuracy above 98% which is far much higher when compared to traditional rule based method. At the end of the paper, some future trends have been mentioned like edge-based vision system, explainable artificial intelligence, and Industry 4.0 integration which will likely characterize the future of intelligent inspection systems.