AI-Enhanced Visual Inspection Systems for Robust Detection of Product Packaging Defects in Manufacturing Environments

AI-enhanced visual inspection systems are important for ensuring product quality and customer satisfaction, particularly in automated manufacturing environments where defects can disrupt supply chains and lead to contamination risks. This study applies the YOLOv7 object detection algorithm for detecting packaging defects such as scratches holes, and deformations in jar lids. The dataset contains 1859 jar lids, on average 11 per image, categorized into intact (962) versus damaged (897) jar lids. After optimizing for class imbalance and performance, the system achieved precision rates of $91.7 \%$ overall and $3.5 \%$ for the ‘damaged’ class, demonstrating robustness in challenging environments. This work presents a scalable AI-based solution to improve defect detection efficiency in manufacturing.