review · Procedia Computer Science
Computer vision enables automated systems to perceive, analyse, and interpret visual data from images and video streams. Across industrial settings, computer vision pairs with machine vision hardware to perform reliable inspection of high-end products, manage quality control processes, and collect operational data. Artificial intelligence methods, notably machine learning and deep learning, play an essential role alongside standard colour recognition techniques in processing visual information extracted from industrial environments. Examining two real-time quality control deployments highlights current progress in translating artificial vision research into practical industrial use. However, critical analysis of these cases reveals existing technical shortcomings, which point to specific operational needs and guide future development in automated inspection.
Industrial manufacturing relies heavily on consistent inspection to ensure product quality and reduce defects. By integrating artificial intelligence and visual recognition systems into production lines, manufacturers can automate oversight in real time. Understanding the technical gaps in current implementations helps industry professionals and engineers refine automated inspection tools to build more dependable manufacturing systems.
The review addresses real-time automated quality control and visual inspection for high-end product manufacturing. Prospective users include industrial manufacturers, factory automation specialists, and quality assurance teams. Because the work is a review evaluating two existing industrial case studies to identify shortcomings and recommend research directions, the insights represent an applied review stage rather than a standalone market-ready product.
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Computer Vision (CV) provides computers with the ability to perceive, analyze, and understand the content of images and videos. The applications are numerous and range from medical diagnosis to industrial quality control and special effects. CV uses different technologies and techniques to extract relevant information from a large number of images acquired via Machine Vision (MV) components. In industry, CV and MV are combined for trustworthy inspection of high-end products, quality control, and data collection applications. In this article, first, we present the CV and MV with a focus on the convenient artificial intelligence tools that may bused, such as machine & deep learning, and also the common color recognition methods. Second, we consider two industrial applications of artificial vision, especially CV, in real-time quality control to review their scientific valorization based on the primary findings of the studies carried out. Finally, we critically analyzed their findings to identify shortcomings, make recommendations, and open up new horizons for future research.
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DOI: 10.1016/j.procs.2023.12.195
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