面向变电站巡检的特定任务导向型语义图像通信系统
A Specific Task-oriented Semantic Image Communication System for substation patrol inspection
AI总结：
针对变电站巡检机器人在弱信号边缘区域图像传输失败的问题，提出面向特定任务的语义图像通信系统STSCI，通过语义增强新范式保障关键信息清晰度，低比特率及低信噪比下性能优于传统方案。
摘要：
智能巡检机器人广泛应用于变电站巡检工作，可通过巡视变电站并回传场景图像协助排查潜在安全隐患。但在巡检部分信号薄弱的边缘区域时，场景图像无法成功传输以用于隐患排查，极大降低了机器人日常工作质量。为解决该问题，本文设计了面向图像的特定任务导向型语义通信系统STSCI，该系统涵盖语义特征提取、传输、恢复与增强环节，可在弱信号条件下获取智能机器人传回的更清晰图像。受变电站巡检任务仅需图像部分特定细节这一特点启发，我们针对该特定任务提出一种语义增强新范式，确保在低比特率或低信噪比场景下关键语义信息的清晰度。基于真实场景的仿真实验表明，在低比特率甚至低信噪比的变电站巡检任务中，本文的STSCI系统性能整体优于传统的基于图像压缩、基于信道编码的系统及其他语义通信系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
