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

Abstract

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 up-sampled 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.