AI 中文总结
针对车联网图像传输的效率与保真度瓶颈，本文提出基于GAN的语义通信框架，通过发送端语义优先级保留与接收端多尺度重建模块，在Cityscapes数据集上优于现有方法且信道适应性稳定。
AI 中文摘要
针对车联网中的协同感知，本文提出一种基于生成对抗网络（GAN）的语义通信框架，以解决传统通信系统在带宽受限与信道动态条件下视觉数据传输的效率与保真度瓶颈。在发送端，该框架采用金字塔注意力网络提取语义标签图，并引入语义优先级保留机制，依据驾驶安全为不同语义类别分配差异化权重，指导比特分配与损失函数设计。在接收端，设计了融合由粗到细多分辨率生成器与多尺度判别器的图像重建模块，结合时间一致性分支、空间金字塔池化与类别感知卷积层，从受损的语义标签中实现高质量图像的高保真重建。该模型采用对抗损失、特征匹配损失与感知损失的组合进行训练，有效提升生成图像的语义一致性与视觉真实性。在Cityscapes数据集上的实验结果表明，所提方法在语义分割准确率与重建图像质量上均优于现有同类方法，且在加性高斯白噪声（AWGN）与瑞利（Rayleigh）信道下保持稳定的重建性能。
英文摘要
For cooperative perception in the internet of vehicles, this paper proposes a generative adversarial network-based semantic communication framework to address the efficiency and fidelity bottlenecks of traditional communication systems in visual data transmission under limited bandwidth and dynamic channel conditions. At the transmitter, the framework adopts a pyramid attention network to extract semantic label maps and introduces a semantic priority preservation mechanism. It assigns differentiated weights to distinct semantic categories based on driving safety, guiding bit allocation and loss function design. At the receiver, an image reconstruction module integrating a coarse to-fine multi-resolution generator and multi-scale discriminator is designed. Combined with the temporal consistency branch, spatial pyramid pooling and class-aware convolutional layers, it achieves high-fidelity reconstruction of high-quality images from corrupted semantic labels. The model is trained with combined adversarial, feature matching and perceptual losses, effectively improving semantic consistency and visual realism of generated images. Experimental results on the Cityscapes dataset show that the proposed method outperforms existing counterparts in both semantic segmentation accuracy and reconstructed image quality, and maintains stable reconstruction performance under AWGN and Rayleigh channels.
Comments8 pages, 7 figures
