Embedded Planogram Compliance Control System
M. Erkin Y\"ucel, Serkan Topalo\u{g}lu, Cem \"Unsalan

TL;DR
This paper presents an embedded system using computer vision and deep learning for planogram compliance in retail, featuring energy harvesting for long-term autonomous operation.
Contribution
It introduces a complete embedded system with novel integration of object detection, compliance control, and energy harvesting for retail applications.
Findings
Achieved F1 scores of 0.997 for object detection and 1.0 for compliance control.
System can operate up to two years on battery alone.
Energy harvesting extends operational duration further.
Abstract
The retail sector presents several open and challenging problems that could benefit from advanced pattern recognition and computer vision techniques. One such critical challenge is planogram compliance control. In this study, we propose a complete embedded system to tackle this issue. Our system consists of four key components as image acquisition and transfer via stand-alone embedded camera module, object detection via computer vision and deep learning methods working on single board computers, planogram compliance control method again working on single board computers, and energy harvesting and power management block to accompany the embedded camera modules. The image acquisition and transfer block is implemented on the ESP-EYE camera module. The object detection block is based on YOLOv5 as the deep learning method and local feature extraction. We implement these methods on Raspberry…
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Taxonomy
TopicsIndustrial Vision Systems and Defect Detection · IoT-based Smart Home Systems · Advanced Neural Network Applications
