Introduction to Target Instance Segmentation Method Based on Deep Learning

With the rapid development of high-tech technologies in the 21st century, society is increasingly moving toward intelligentization. Intelligentization refers to endowing machines with human-like capabilities, and among these, the foremost is the ability to perceive the external environmentwhat is known as computer vision. Possessing perceptual capabilities means being able to distinguish and categorize elements within an environment, identifying those with similar attributes while differentiating those with distinct attributes. This involves organizing objects in the environment into cohesive units under unified concepts, enabling recognition of diverse entities. This process requires target instance segmentation methods based on deep learning. This paper primarily summarizes and analyzes early and mainstream approaches to target instance segmentation using deep learning, thoroughly examining existing research to guide future work in this field. Through analysis, this paper finds that diverse innovative strategies, such as parallel mask assembly (YOLACT), instance classification via spatial grids (SOLO), polar coordinate representation (PolarMask), and unified mask classification (MaskFormer), can effectively combine instance distinction with pixel-level classification.