Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/5/1850 |
| _version_ | 1797264858086899712 |
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| author | José Silva Pedro Coelho Luzia Saraiva Paulo Vaz Pedro Martins Alfonso López-Rivero |
| author_facet | José Silva Pedro Coelho Luzia Saraiva Paulo Vaz Pedro Martins Alfonso López-Rivero |
| author_sort | José Silva |
| collection | DOAJ |
| description | Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems. |
| first_indexed | 2024-04-25T00:35:34Z |
| format | Article |
| id | doaj.art-8d80f4b1b5964eb8a96a257ce9004f2c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:35:34Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8d80f4b1b5964eb8a96a257ce9004f2c2024-03-12T16:39:12ZengMDPI AGApplied Sciences2076-34172024-02-01145185010.3390/app14051850Validating the Use of Smart Glasses in Industrial Quality Control: A Case StudyJosé Silva0Pedro Coelho1Luzia Saraiva2Paulo Vaz3Pedro Martins4Alfonso López-Rivero5Research Centre in Digital Services (CISeD), Instituto Politécnico de Viseu, 3504-510 Viseu, PortugalDepartment of Informatics Engineering, Instituto Politécnico de Viseu, 3504-510 Viseu, PortugalDepartment of Informatics Engineering, Instituto Politécnico de Viseu, 3504-510 Viseu, PortugalResearch Centre in Digital Services (CISeD), Instituto Politécnico de Viseu, 3504-510 Viseu, PortugalResearch Centre in Digital Services (CISeD), Instituto Politécnico de Viseu, 3504-510 Viseu, PortugalComputer Science Faculty, Pontifical University of Salamanca, 37002 Salamanca, SpainEffective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems.https://www.mdpi.com/2076-3417/14/5/1850quality inspectionsmart manufacturingobject detectioncomputer vision |
| spellingShingle | José Silva Pedro Coelho Luzia Saraiva Paulo Vaz Pedro Martins Alfonso López-Rivero Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study Applied Sciences quality inspection smart manufacturing object detection computer vision |
| title | Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study |
| title_full | Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study |
| title_fullStr | Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study |
| title_full_unstemmed | Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study |
| title_short | Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study |
| title_sort | validating the use of smart glasses in industrial quality control a case study |
| topic | quality inspection smart manufacturing object detection computer vision |
| url | https://www.mdpi.com/2076-3417/14/5/1850 |
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