Survey of Image Semantic Segmentation Methods Based on Deep Neural Network

Abstract

Image semantic segmentation is a hot research topic in the field of computer vision in recent years. With the rise of deep learning technology, image semantic segmentation and deep learning technology are integrated and developed, which has made significant progress. It is widely used in practical scenarios such as unmanned driving, intelligent security, intelligent robot, human-computer interaction. Firstly, several deep neural network models for image semantic segmentation are introduced, and then the existing mainstream deep neural network-based image semantic segmentation methods are introduced. According to the differences of implementation technologies, image semantic segmentation methods are classified, and the technical characteristics, advantages and disadvantages of representative algorithms are analyzed and summarized. After that, the common datasets and performance evaluation indexes of image semantic segmentation are summarized, and the experimental results of classic semantic segmentation methods are compared on this basis. Finally, the future feasible research directions in the field of semantic segmentation are prospected.