6. Applying machine learning and computer vision for Planogram compliance evaluation in retail environments

Tóm tắt

This paper presents a novel approach for automated planogram compliance assessment in retail environments, with a focus on the Vietnamese market. Addressing the limitations of manual inspection methods—which are time-consuming, error-prone, and difficult to scale—the proposed system integrates recent advances in computer vision and machine learning. Specifically, the method leverages state-of-the-art object detection models, YOLOv11 and YOLOv12, trained on annotated shelf images collected from real retail settings. Detected products are spatially organized using the DBSCAN clustering algorithm, while the Hungarian algorithm is employed to match detected layouts with predefined planograms and compute compliance scores. Experimental results demonstrate high detection accuracy and reliable compliance evaluation, even under complex retail conditions. The combination of advanced YOLO models with spatial reasoning techniques proves effective in handling challenges unique to the Vietnamese retail landscape, such as inconsistent shelf organization and varied packaging. This work contributes a scalable, accurate, and practical solution for enhancing retail execution and operational efficiency.