A Review of Research on Instance Segmentation Based on Deep Learning

The field of machine vision has witnessed a significant surge in the application of deep learning technology, as researchers increasingly leverage its capabilities in their work. While deep learning has been extensively used in object detection and semantic segmentation, research on deep learning-based instance segmentation has gained significant traction only in recent years. Instance segmentation is a computer vision task that is closest to the real human visual experience and provides a deep understanding of image scenes. Instance segmentation encompasses more than just pixel-level segmentation of various object categories; it also involves the ability to distinguish and separate individual instances within each category. It can be widely applied in fields such as autonomous driving, assisted medical treatment, and remote sensing imaging. This article systematically summarizes some typical instance segmentation models in two parts: two-stage and single-stage, analyzes and compares the advantages and disadvantages of different algorithms, and conducts performance tests on the COCO dataset. This article also provides a brief introduction to the COCO dataset and instance segmentation evaluation indicators. Finally, the possible future development directions and challenges faced by instance segmentation are discussed.