Digital Twins play a crucial role in bridging the physical and virtual worlds. Given the dynamic and evolving characteristics of the physical world, a huge volume of data transmission and exchange is necessary to attain synchronized updates in the virtual world. In this paper, we propose a semantic communication framework based on You Only Look Once (YOLO) to construct a virtual apple orchard with the aim of mitigating the costs associated with data transmission. Specifically, we first employ the YOLOv7-X object detector to extract semantic information from captured images of edge devices, thereby reducing the volume of transmitted data and saving transmission costs. Afterwards, we quantify the importance of each semantic information by the confidence generated through the object detector. Based on this, we propose two resource allocation schemes, i.e., the confidence-based scheme and the artificial intelligence-generated scheme, aimed at enhancing the transmission quality of important semantic information. The proposed diffusion model generates an optimal allocation scheme that outperforms both the average allocation scheme and the confidence-based allocation scheme. Moreover, to obtain semantic information more effectively, we enhance the detection capability of the YOLOv7-X object detector by introducing new Efficient Layer Aggregation Network-HorNet (ELAN-H) and SimAM attention modules, while reducing the model parameters and computational complexity, making it easier to run on edge devices with limited performance. The numerical results indicate that our proposed semantic communication framework and resource allocation schemes significantly reduce transmission costs while enhancing the transmission quality of important information in communication services.

翻译：数字孪生在连接物理世界与虚拟世界中发挥着关键作用。鉴于物理世界的动态演进特性，实现虚拟世界的同步更新需要海量数据的传输与交换。本文提出一种基于YOLO（You Only Look Once）的语义通信框架，用于构建虚拟苹果园，旨在降低数据传输成本。具体而言，我们首先采用YOLOv7-X目标检测器从边缘设备采集的图像中提取语义信息，从而减少传输数据量并节省传输成本。随后，通过目标检测器生成的置信度量化各语义信息的重要性。基于此，我们提出两种资源分配方案，即基于置信度的方案和人工智能生成方案，旨在提升重要语义信息的传输质量。所提出的扩散模型可生成最优分配方案，其性能优于平均分配方案和基于置信度的方案。此外，为更高效地获取语义信息，我们通过引入新型高效层聚合网络-HorNet（ELAN-H）和SimAM注意力模块，在增强YOLOv7-X目标检测器检测能力的同时，降低模型参数量和计算复杂度，使其更易于在性能有限的边缘设备上运行。数值结果表明，本文提出的语义通信框架及资源分配方案在显著降低传输成本的同时，有效提升了通信服务中重要信息的传输质量。