AUTOMATED DEFECT PRODUCT DETECTION

Abstract

Traditional visual inspection of production lines is tedious, error-prone, and costly, and therefore inappropriate for high-volume production environments. The aim of this project is to investigate the potential of employing computer vision and Convolutional Neural Networks (CNNs) to produce automatic fault detection on manufacturing lines. Since I was the AI engineer in charge of this project, model training, backend development, model testing, accuracy improvement, hyperparameter tuning, fine tune operations, and dataset expansion were activities that I was tasked with. The adopted strategy was large-scale testing of a series of architectures like Anomalib framework implementations, CNN models, HRNet, YOLO variants, and other deep learning methods. All the models were thoroughly evaluated on performance metrics including accuracy, precision, recall, F1-score, mean Average Precision (mAP), and processing speeds. Key results indicate notable performance improvements in defect detection. Precise models achieved high accuracy with precision and recall maximized metrics with processing rates compatible with real-time industrial operations. The mAP scores and F1-scores validate consistent performance across different classes of defects and categories of products. Findings confirm the technical feasibility of implementing computer vision-based quality assurance systems in factories with the potential to transform factory quality assurance by eliminating human mistakes and saving huge sums of money through auto-defect detection.