Advanced Real-Time Surface DefectDetection in Industrial Products Using Machine Vision
Surface defect detection has gained attention as a challenging task in recent decades. This paper focuses on developing an algorithm capable of identifying defects in various types of industrial products, while exhibiting real-time performance, providing users with the ability to monitor the entire process from circle detection to the defect analysis. This paper was created in collaboration with the Beia International Consult team[1] which conducted the development of a multi material and multi defect detection and anomaly prediction system based on machine vision, artificial intelligence and IoT (ERA-Net MANUNET).
Authors
- Nicolae Goga (ORCID: https://orcid.org/0000-0003-3812-9646)
- Bianca Georgiana Dinu
- Florin Baciu (ORCID: https://orcid.org/0000-0002-0574-6149)
- Anca Cristina Stanciu (ORCID: https://orcid.org/0000-0002-6015-7777)
- Cătălin Alexandru (ORCID: https://orcid.org/0000-0003-1823-4537)
- România
- Radu Popa (ORCID: https://orcid.org/0000-0002-9075-2286)
- George Suciu (ORCID: https://orcid.org/0000-0001-8455-6177)
- Bucharest
- Ioana Petre (ORCID: https://orcid.org/0000-0003-2641-5119)
- Hermann Josef Anton
- B.C. Perino
- M. Butolo
Institutions
- Universitatea Națională de Știință și Tehnologie Politehnica București (RO)
Publication Details
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1109/smartblock4africa61928.2024.10779491
- Primary Topic
- Industrial Vision Systems and Defect Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00