Intelligent inspection robots are widely used in substation patrol
inspection, which can help check potential safety hazards by patrolling the
substation and sending back scene images. However, when patrolling some
marginal areas with weak signal, the scene images cannot be sucessfully
transmissted to be used for hidden danger elimination, which greatly reduces
the quality of robots'daily work. To solve such problem, a Specific
Task-oriented Semantic Communication System for Imag-STSCI is designed, which
involves the semantic features extraction, transmission, restoration and
enhancement to get clearer images sent by intelligent robots under weak
signals. Inspired by that only some specific details of the image are needed in
such substation patrol inspection task, we proposed a new paradigm of semantic
enhancement in such specific task to ensure the clarity of key semantic
information when facing a lower bit rate or a low signal-to-noise ratio
situation. Across the reality-based simulation, experiments show our STSCI can
generally surpass traditional image-compression-based and channel-codingbased
or other semantic communication system in the substation patrol inspection task
with a lower bit rate even under a low signal-to-noise ratio situation.

本文介绍了针对在信号弱的场景下检测智能检测机器人拍摄的电力变电站场景图像的困难问题，通过提出一种特定任务定向的语义通信系统（STSCI）解决了弱信号下机器人发送图像不清晰的问题，并提出了一种语义增强新范式以确保在低比特率或低信噪比情况下保留关键语义信息。模拟实验表明，该 STSIC 相比以图像压缩为基础或以通道编码为基础或其他语义通信系统，能够在低比特率甚至在低信噪比的情况下在电力变电站检测任务中取得更好的效果。

变电站巡检专用语义图像传输系统

A Specific Task-oriented Semantic Image Communication System for substation patrol inspection

Although analog semantic communication systems have received considerable
attention in the literature, there is less work on digital semantic
communication systems. In this paper, we develop a deep learning (DL)-enabled
vector quantized (VQ) semantic communication system for image transmission,
named VQ-DeepSC. Specifically, we propose a convolutional neural network
(CNN)-based transceiver to extract multi-scale semantic features of images and
introduce multi-scale semantic embedding spaces to perform semantic feature
quantization, rendering the data compatible with digital communication systems.
Furthermore, we employ adversarial training to improve the quality of received
images by introducing a PatchGAN discriminator. Experimental results
demonstrate that the proposed VQ-DeepSC is more robustness than BPG in digital
communication systems and has comparable MS-SSIM performance to the DeepJSCC
method.

本文提出了一种基于深度学习（DL）和向量量化（VQ）的语义通信系统 VQ-DeepSC，通过卷积神经网络（CNN）提取图像的多尺度语义特征并利用多尺度语义嵌入空间实现语义特征量化，从而使数据兼容数字通信系统，同时通过对抗训练引入 PatchGAN 鉴别器来提高接收图像的质量。实验结果表明，与 BPG 在数字通信系统方面相比，所提出的 VQ-DeepSC 具有更强的鲁棒性，并且在 MS-SSIM 性能方面与 DeepJSCC 方法相当。