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 Pi 4, NVIDIA Jetson Orin Nano, and NVIDIA Jetson AGX Orin as single board computers. The planogram compliance control block utilizes sequence alignment through a modified Needleman-Wunsch algorithm. This block is also working along with the object detection block on the same single board computers. The energy harvesting and power management block consists of solar and RF energy harvesting modules with suitable battery pack for operation. We tested the proposed embedded planogram compliance control system on two different datasets to provide valuable insights on its strengths and weaknesses. The results show that our method achieves F1 scores of 0.997 and 1.0 in object detection and planogram compliance control blocks, respectively. Furthermore, we calculated that the complete embedded system can work in stand-alone form up to two years based on battery. This duration can be further extended with the integration of the proposed solar and RF energy harvesting options.

翻译：零售领域存在若干开放且有挑战性的问题，可受益于先进的模式识别与计算机视觉技术。其中一项关键挑战是货架陈列合规控制。本研究提出了一套完整的嵌入式系统以解决该问题。该系统包含四个核心组件：通过独立嵌入式摄像头模块进行图像采集与传输、基于单板计算机的物体检测（采用计算机视觉与深度学习方法）、同样运行于单板计算机的货架陈列合规控制方法，以及为嵌入式摄像头模块配套的能量采集与电源管理模块。图像采集与传输模块基于ESP-EYE摄像头模块实现。物体检测模块采用YOLOv5作为深度学习方法并结合局部特征提取。我们在树莓派4、英伟达Jetson Orin Nano和英伟达Jetson AGX Orin等单板计算机上实现了这些方法。货架陈列合规控制模块通过改进的Needleman-Wunsch算法进行序列比对，该模块与物体检测模块在相同单板计算机上协同工作。能量采集与电源管理模块包含太阳能和射频能量采集单元及配套电池组。我们使用两个不同数据集对所提出的嵌入式货架陈列合规控制系统进行测试，以深入解析其性能优劣。结果表明，该方法在物体检测和货架陈列合规控制模块中分别达到0.997和1.0的F1分数。此外，经计算，整个嵌入式系统可独立运行长达两年（基于电池供电），若集成所提出的太阳能和射频能量采集方案，该运行时长可进一步延长。