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This invention relates to a robust image semantic perception adaptive masking transmission method for vehicle-to-everything (V2X) networks, comprising: Step 1: The transmitting end performs semantic analysis on the original input image, extracts high-precision semantic features, and identifies the regions of interest (ROI) and non-ROI regions of interest (NROI); Step 2: A Boolean mask is dynamically generated based on the semantic segmentation result and SNR; Step 3: The ROI features are directly masked based on the Boolean mask; Subsequently, the processed feature map is fed into a semantic encoder to extract multi-scale semantic features; Step 4: Power normalization is performed, dynamic feature encoding is performed, and then the encoded signal is transmitted through a typical analog wireless channel, and noise is superimposed before reception and recovery; Step 5: The receiving end receives the noisy feature signal, performs channel fading compensation and preliminary denoising, and sends the decoded features into a semantic decoder; Through progressive upsampling and multi-level semantic reconstruction, multi-scale features are fused, and finally, a high-quality reconstructed image is recovered and output.