Abstract
Ensuring product quality and minimizing defects is crucial in today's manufacturing industry. Traditional manual inspections are labor-intensive and error prone.This paper describes a system designed to identify defects automatically the YOLOv5 algorithm, known for its accuracy and speed. High-resolution images of products are processed with YOLOv5 to identify defects like scratches, dents, and deformations. This system enhances sorting and quality assurance, improving efficiency and consistency. Experimental results show YOLOv5 superior performance in detection accuracy and speed compared to traditional methods, exploring the feasibility of combining machine learning and image processing within manufacturing.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Innovative Science and Research Technology (IJISRT)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
