Computer Vision-Based Product Quality Inspection And Novel Counting System

Abstract

In this study, we aim 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 YOLO v8 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 1kg, 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.