Survey Progress on Image Instance Segmentation Methods of Deep Convolutional Neural Network

Abstract

Image instance segmentation is an important part of image processing and computer vision technology about image understanding.With the development of deep learning and deep convolutional neural network,image instance segmentation method based on deep convolutional neural network has made great progress.Instance segmentation task is actually the combination of target detection and semantic segmentation,which can complete the task of recognizing the target contour in the image at thepixel level.Instance segmentation can not only locate the position of the object in the image,segment all the objects from the pixel level,but also mark different individuals of the same category in the image,which is not only the pixel level segmentation of the image,but also the instance level understanding.Firstly,the reason of image segmentation and the function of deep convolution neural network are described.Then,according to the process and characteristics of image instance segmentation methods,the research progress of image instance segmentation is introduced from two-stage and single-stage perspectives,and the advantages and disadvantages of the two methods are described in detail.Then,the design ideas of region,feature extraction and mask are summarized.In addition,the performance evaluation criteria and common public data sets of image instance segmentation methods are summarized,and on this basis,the segmentation accuracy of mainstream image instance segmentation models is compared and evaluated.Finally,it points out the problems and solutions of the current image instance segmentation,summarizes the development of image instance segmentation and prospects for the future.

Keywords

• instance segmentation|deep convolutional neural network|object detection|semantic segmentation|two stage|single stage