Hybrid Approach for Shelf Monitoring and Planogram Compliance (Hyb-SMPC) in Retails Using Deep Learning and Computer Vision

Abstract

In retail management, the continuous monitoring of shelves to keep track of the availability of the products and following proper layout are the two important factors that boost the sales and improve customer’s level of satisfaction. The studies conducted earlier were either performing shelf monitoring or verifying planogram compliance. As both the activities are important, to tackle this problem, we presented a deep learning and computer vision-based hybrid approach called Hyb-SMPC that deals with both activities. The Hyb-SMPC approach consists of two modules: The first module detects fine-grained retail products using one-stage deep learning detector. For the detection part, the comparison of three deep learning-based detectors, You Only Look Once (YOLO V4), YOLO V5, and You Only Learn One Representation (YOLOR), is provided and the one giving the best result will be selected. The selected detector will perform detection of different categories of SKUs and racks. The second module performs planogram compliance; for this purpose, the company-provided layout is first converted to JavaScript Object Notation (JSON) and then the matching is performed with the postprocessed retail images. The compliance reports will be generated at the end for indicating the level of compliance. The approach is tested in both quantitative and qualitative manners. The quantitative analysis demonstrates that the proposed approach achieved an accuracy up to 99% on the provided dataset of retail, whereas the qualitative evaluation indicates increase in sales and customers’ satisfaction level.