Recommendations are formulated for choosing an optimal machine vision architecture for different types of production, and promising directions for further development of the technology in the context of industrial digital transformation are identified.
Abstract
This article examines the application of machine vision systems for automated quality control at manufacturing enterprises. It analyzes the current state of the market, the hardware structure of visual inspection systems, and the technological capabilities of 2D, 3D, hyperspectral, and intelligent machine vision for defect detection, dimensional measurement, assembly verification, marking inspection, and packaging control. A classification of production tasks based on the feasibility of machine vision implementation is proposed. Special attention is paid to the selection of cameras, lenses, lighting, image processing algorithms, and integration with programmable logic controllers, MES, and ERP systems. The article presents accuracy indicators, methods for evaluating algorithm performance, and recommendations for reducing false rejects and missed defects. Based on the analysis of practical experience from Russian and international enterprises, recommendations are formulated for choosing an optimal machine vision architecture for different types of production, and promising directions for further development of the technology in the context of industrial digital transformation are identified.
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.
M. Bianchi· International Journal of Mod...· 0 citations
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
Automated mechanical inspection is an important requirement in modern manufacturing industries for detecting dimensional errors, surface defects, cracks, missing components, and improper assembly. Conventional inspection methods often depend on manual operators or PC-based image-processing systems, which can introduce inspection delays and inconsistent results. This work proposes an FPGA-based machine vision system for automated mechanical inspection, combining image acquisition, preprocessing, feature extraction, defect detection, and decision-making on a reconfigurable FPGA platform. A camera captures images of mechanical components placed in a controlled inspection area. The acquired image is converted into a suitable digital format and processed using FPGA-based image-processing algorithms such as grayscale conversion, noise filtering, thresholding, edge detection, and morphological operations. Extracted features are compared with predefined dimensional and quality criteria to identify defective components. The FPGA provides parallel processing and low-latency operation, making the proposed system suitable for real-time industrial inspection. The system can generate a pass/fail decision and activate a sorting mechanism through suitable control interfaces. The proposed architecture offers improved processing speed, reduced dependence on external computers, and flexible hardware implementation. It can be applied to inspection of gears, shafts, bearings, castings, welds, electronic-mechanical assemblies, and other manufactured components.
Jeyasudha S, Radhakrishnan B, Sowmiya C et al.· International Research Journ...· 0 citations
The adoption of Industry 4.0 technologies has intensified the demand for reliable quality assurance in industrial packaging processes, particularly in areas where manual visual inspection is still prevalent. In an investigation of the production environment, recurrent packaging errors, including missing or duplicated accessories, led to increased rework and quality-related costs. This study presents a semiautomated scene-level packaging inspection approach based on computer vision and artificial intelligence. The proposed solution integrates a deep learning-based visual inspection module with an electromechanical Poka-Yoke system, forming a closed-loop quality assurance mechanism that physically prevents nonconforming packages from advancing along the production line. Experimental results evaluating the visual inspection module showed that a YOLO-based detector achieved a scene-level exact-match accuracy of 99.35% under varying illumination, object configurations, and camera viewpoints, outperforming an RF-DETR baseline. These results demonstrate the suitability of the proposed approach for inline industrial inspection within the production takt-time constraints.
D. Cardozo, A. Loureiro, Leonardo Camelo et al.· IEEE Sensors Letters· 0 citations
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
Aiming at the problems of low efficiency of manual management of electronic components in university laboratories, error-prone inventory, and difficult control of small components, this paper designs an automatic sorting and storage system based on machine vision and embedded control. The system uses Raspberry PI 5B as the main control and K230 as the visual processing unit, uses YOLO11n(You Only Look Once) lightweight algorithm to realize real-time and highprecision detection of 10 types of components, and completes automatic grasping, transportation and classified storage through a three-axis closed-loop manipulator and flexible gripper. Alibaba Cloud IoT(Internet of Things) platform and HarmonyOS mobile APP were used to realize remote monitoring of inventory data. Test results indicate that the system achieves an average recognition accuracy of over 90% for 10 categories of target components, with a 100% success rate in capturing. It can reliably complete the full process of automation from identification to warehousing. This solution provides a reusable technical pathway for the engineering application of machine vision in precision sorting and warehouse management, and also serves as a reference example for the integration of vision‑guided automation systems under the theme of the 2026 International Conference on machine Vision, Detection and 3D Imaging Technology (MVDIT 2026).
Liming Zhu, Yu Liu, Yuehan Wu et al.· International Conference on...· 0 citations
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