A Multi Semantic Image Segmentation Method Based on Improved High Resolution Networks
To address the difficulty of image segmentation in complex outdoor scenes,this paper proposes a multi semantic image segmentation model based on HRNet(HR_DfeNet),which optimizes feature extraction by introducing channel attention and spatial attention modules,designs a high-resolution feature extraction branch by improving the pyramid pooling module and ASPP_M module,and integrates with multiple attention mechanisms.On the Cityscape dataset,HR_DfeNet exhibits varying degrees of segmentation optimization performance compared to traditional segmentation models.
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