Review of object instance segmentation based on deep learning

Di Tian, Yi Han, Bi-Yao Wang, Tian Guan, Hengzhi Gu, Wei Wei

Abstract: As a challenging task in computer vision, instance segmentation has attracted extensive attention in recent years. Able to obtain very rich and refined object information, this technology shows important application value in many fields, such as intelligent driving, medical health, and remote sensing detection. Instance segmentation technology should not only identify the positions of objects but should also accurately mark the boundary of any single instance, which can be defined as solving object detection and semantic segmentation at the same time. Our study gives a detailed introduction to the background of instance segmentation technology, its development and the common datasets in this field, and further deeply discusses key issues appearing in the development of this field, with the future development direction of instance segmentation technology proposed. Our study provides an important reference for future research on this technology