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Computer-Vision-Based Product Quality Inspection and Novel Counting System

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Publicado en:Applied System Innovation vol. 7, no. 6 (2024), p. 127
Autor principal: Lee, Changhyun
Otros Autores: Kim, Yunsik, Kim, Hunkee
Publicado:
MDPI AG
Materias:
Accuracy
Load cells
Deep learning
Defects
Productivity
Quality control
Computer vision
Automation
Manufacturing
Product image
Image processing
Data compression
Efficiency
Cameras
Inspection
Neural networks
Algorithms
Data collection
Rotating bodies
Image quality
Real time
Counting
Cost control
Inventory management
Human error
Inventory
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Resumen:In this study, we aimed to enhance the accuracy of product quality inspection and counting in the manufacturing process by integrating image processing and human body detection algorithms. We employed the SIFT algorithm combined with traditional image comparison metrics such as SSIM, PSNR, and MSE to develop a defect detection system that is robust against variations in rotation and scale. Additionally, the YOLOv8 Pose algorithm was used to detect and correct errors in product counting caused by human interference on the load cell in real time. By applying the image differencing technique, we accurately calculated the unit weight of products and determined their total count. In our experiments conducted on products weighing over 1 kg, we achieved a high accuracy of 99.268%. The integration of our algorithms with the load-cell-based counting system demonstrates reliable real-time quality inspection and automated counting in manufacturing environments.
ISSN:2571-5577
DOI:10.3390/asi7060127
Fuente:Advanced Technologies & Aerospace Database