Multi-branch residual image semantic segmentation combined with inverse weight gated-control

The loss of pixel-level information in the multi-class segmentation task based on the U-net model results in unclear boundaries and low semantic segmentation accuracy. Aiming at this, a deep multi-branch residual Unet (IWG-MRUN) with fused inverse weight gated-control is proposed to improve the quality of image semantic segmentation. Specifically, we first introduce a deep multi-branch residual module, which used parallel convolution mode to capture the contextual feature to extract the detailed features of the input image at a deeper level. Then, we adopt an inverse weight gated-control module to enhance the diversity of up-sampling information by counterclockwise transmitting attention horizontally to improve the restoration accuracy of upsampled image pixels. Finally, to obtain finer granularity features from low spatial resolution images, we adopt the different receptive field pyramid attention mechanisms at the highest level of the U-shaped encoder to capture high-level context information at different scales, thereby improving the accuracy of semantic segmentation. The experimental results show that the segmentation accuracy of the proposed algorithm reaches 91.80% and the CCE loss is reduced to 0.21. When compared to the Unet, BiSeNet, DeeplabV3+ and U-net + BLR model, the pixel accuracy of semantic segmentation is improved by 15.0%, 1.98%, 0.9% and 6.5%, respectively. The semantic segmentation model proposed in this paper provides an end-to-end semantic segmentation capability with the enriched finer granularity features of the target boundary and realizes the accurate segmentation of the objects in different categories.