Semantic Image Segmentation Based on Improved SEGNET Model

Abstract

When applied to semantic image segmentation,the original SEGNET model does not account for the relationship between adjacent pixels in the image,resulting in inconsistent prediction results of pixel categories in the same target.By adding a top-down channel in the SEGNET structure,the multi-scale semantic information of the SEGNET model is enriched,and the accuracy of category prediction for each pixel is improved.The generative adversarial network is added to the model to ensure that the model can consider the relationship between adjacent pixels in space.The experimental results show that the semantic segmentation effect of the improved SEGNET model is significantly improved compared with the original SEGNET model.It can effectively avoid the classification errors in the SEGNET test.