Image Semantic Segmentation Based on Joint Normalization

Abstract

AbstractImage semantic segmentation is an important research direction in image processing, computer vision and deep learning. Semantic segmentation is to classify the image pixel by pixel, so that the original image is divided into semantic segmentation images with specific pixel marks, which is the most challenging in image processing. Based on DSC-JFP (depthwise separable convolution-joint feature pyramid) model, ASPP model and auxiliary network are removed to improve the real-time performance of semantic segmentation. Combined with batch normalization and instance normalization, parallel batch and instance normalization (PBIN) and cascaded batch and instance normalization (CBIN) methods are proposed to improve the effect of semantic segmentation. The experimental results also show that the proposed method improves the real-time performance of semantic segmentation while ensuring the effect of semantic segmentation.KeywordsDeep learningSemantic segmentationJoint normalization