Computer Vision for Quality Assurance in Packaging Process: A Case Study
Abstract
The adoption of Industry 4.0 technologies has intensified the demand for reliable quality assurance in industrial packaging processes, particularly in areas where manual visual inspection is still prevalent. In an investigation of the production environment, recurrent packaging errors, including missing or duplicated accessories, led to increased rework and quality-related costs. This study presents a semiautomated scene-level packaging inspection approach based on computer vision and artificial intelligence. The proposed solution integrates a deep learning-based visual inspection module with an electromechanical Poka-Yoke system, forming a closed-loop quality assurance mechanism that physically prevents nonconforming packages from advancing along the production line. Experimental results evaluating the visual inspection module showed that a YOLO-based detector achieved a scene-level exact-match accuracy of 99.35% under varying illumination, object configurations, and camera viewpoints, outperforming an RF-DETR baseline. These results demonstrate the suitability of the proposed approach for inline industrial inspection within the production takt-time constraints.