Aug 2026· Italian National Conference on Sensors· Vol 26, pp. 5432· 0 citations· 30 references
TL;DR
The present study supports the internal consistency and practical relevance of EF-IVIS, while its usability, repeatability, transferability, and complete lifecycle effectiveness require independent prospective validation.
Abstract
Industrial vision inspection systems increasingly combine machine vision, industrial metrology, artificial intelligence, industrial communication, and manufacturing automation within integrated cyber-physical production environments. Although extensive research addresses individual technologies, comparatively limited methodological support is available for coordinating engineering decisions across the complete development lifecycle of such systems. This paper presents the Engineering Framework for Intelligent Vision Inspection Systems (EF-IVIS), a domain-specific systems engineering approach organized into seven interconnected engineering domains: inspection requirements, image acquisition, illumination engineering, measurement strategy, artificial intelligence, industrial integration, and validation and continuous improvement. EF-IVIS was developed through a bottom-up synthesis of established engineering principles and four complementary industrial investigations addressing AI-based defect detection, measurement strategies, illumination conditions, and PLC-supported integration. The empirical assessment distinguishes direct experimental evidence, direct industrial evidence, cross-cutting retrospective evidence, and methodological support. Direct evidence supports selected aspects of image acquisition and the illumination engineering, measurement strategy, artificial intelligence, and industrial integration domains, whereas inspection requirements and validation and continuous improvement are supported primarily through cross-study synthesis and established systems engineering principles. The framework introduces cross-domain dependencies, application-specific Decision Gates, and feedback mechanisms for iterative engineering assessment. A worked industrial-integration example demonstrates how reported technical evidence can be translated into Decision Gate criteria without treating the reported values as universal thresholds. The present study supports the internal consistency and practical relevance of EF-IVIS, while its usability, repeatability, transferability, and complete lifecycle effectiveness require independent prospective validation.
Computer vision (CV) systems driven by artificial intelligence (AI) are increasingly replacing manual and conventional rule-based inspection procedures in industrial quality inspection, enabling automated, real-time, and data-driven decision-making based on visual data. Conventional inspection procedures are usually limited in terms of scalability, human-related errors, and high operational costs, which drives the increasing reliance on smart vision-based technologies. A practical, practice-oriented review of AI-based computer vision systems for industrial quality control is provided in this paper, with emphasis on real-world deployment issues and performance aspects. Two representative industrial case studies are examined. The first investigates real-time extrusion monitoring in robotic building construction, where geometric deviations, bead-width variation, surface irregularities, and process inconsistencies are detected during material deposition using vision-based monitoring and image-processing pipelines. The second case study focuses on automated inspection of bolts and screws in manufacturing lines, addressing presence detection, orientation recognition, and defect classification under high-speed production conditions. In both cases, widely adopted vision and AI techniques, including image-processing pipelines, convolutional neural networks, and edge-computing hardware, are discussed and compared. The analysis shows that AI-enabled computer vision systems can outperform traditional rule-based or manual solutions in terms of inspection accuracy, consistency, and throughput when they are supported by reliable acquisition, representative data, and robust industrial integration. Nevertheless, challenges related to dataset quality, model generalization, lighting variability, and real-time computational constraints remain critical in industrial environments. In conclusion, AI-based computer vision plays a central enabling role in intelligent quality inspection within the context of Industry 5.0. Future research should focus on adaptive model capabilities, tighter integration with cyber-physical systems, and scalable deployment strategies to achieve reliable and autonomous inspection across diverse industrial sectors.
Maaz A. Khan, C. Vasques, A. Cavadas· Encyclopedia· 0 citations
Experimental evaluation demonstrates significant improvements in inspection accuracy, fault classification, decision-making, maintenance prediction, reduced downtime, and lower human intervention, providing a foundation for next-generation intelligent industrial automation.
O. Dahl, K. Nygaard· International Journal of Mod...· 0 citations
With automated manufacturing advancing toward high precision and high efficiency, the integration of artificial intelligence, machine vision and industrial robots has become the core pillar of intelligent manufacturing. Traditional manual inspection and scheduled maintenance suffer from high missing detection rates, delayed response and excessive operating costs, failing to satisfy strict quality control standards of modern manufacturing. Deep learning empowers machine vision to extract defect features and realize automatic classification efficiently, while industrial robots break free from fixed programming to achieve autonomous perception and decision-making. Supported by multi-sensor data fusion and machine learning, predictive maintenance enables early equipment fault identification and drastically cuts unplanned downtime. Vision-guided robotic inspection systems outperform human operators in both detection speed and precision. This paper systematically sorts out technical routes, system architectures and engineering practices of the two major application scenarios, and discusses cutting-edge directions for collaborative integration of vision and robotic technologies, providing systematic references for engineering implementation and relevant research layout.
Machine vision has become a key technology in modern industrial automation, enabling fully automated quality inspection in robotic manufacturing systems. Unlike traditional human inspection methods, machine vision offers higher accuracy, consistency, speed, and lower operational costs. These systems use cameras, lighting, lenses, image processing, pattern recognition, and AI techniques to detect defects, verify dimensions, classify products, and monitor manufacturing processes in real time. This survey reviews machine vision-based quality inspection methods in robotic manufacturing up to 2019, covering major applications in industries such as automotive, electronics, aerospace, pharmaceuticals, and food processing. It highlights advancements in feature extraction, defect detection algorithms, classification models, and robotic integration frameworks. The study shows that machine vision significantly improves inspection accuracy, reduces cycle time, and enhances manufacturing consistency, while also discussing challenges such as illumination changes, computational complexity, and system adaptability. Overall, machine vision plays a vital role in smart manufacturing and Industry 4.0 production systems.
Louis Pouzin, J. Arsac· International Journal of Int...· 0 citations
This survey categorizes AI-based vision systems by their functional objectives rather than algorithmic classification, and organizes prior work by functional roles and practical deployment considerations, rather than algorithm-centric evaluations, to provide practitioners with actionable insights for industrial adoption.
Minjung Kim, Hwan-Sik Yoon· AI for Engineering· 0 citations
This review presents a comprehensive analysis of industrial hazard identification and automated safety control systems by synthesizing research published between 2013 and 2026, and explores the role of Safety 4.0 and Industry 5.0 paradigms in promoting human-centric, sustainable, and resilient industrial operations.
T. M. Hossain, M. Chowdhury, K. M. M. F. Mithon et al.· International Journal of Mul...· 0 citations
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