深度学习的图像实例分割方法综述

实例分割是一项具有挑战性的任务,需要同时进行实例级和像素级的预测,在自动驾驶、视频分析、场景理解等方面应用广泛.近年来,基于深度学习的实例分割方法迅速发展,如两阶段检测器Faster R-CNN扩展出的聚焦于网络的精度而非速度的强大实例分割基准Mask R-CNN,一度成为实例分割的标杆.利用高速检测的单阶段检测器延伸出的实例分割算法YOLACT填补了实时实例分割模型的空白,具有较高的研究和应用价值.本文首先对实例分割算法进行了类别划分,然后对一些代表性的算法及其改进算法进行了深入分析,并阐述了相关算法的优缺点,最后对实例分割方法未来的发展进行了展望.

Survey of Image Instance Segmentation Methods Using Deep Learning

Instance segmentation is a challenging task that requires both instance-level and pixel-level prediction,and is widely used in autonomous driving,video analysis,and scene understanding.In recent years,instance segmentation methods based on deep learning have developed rapidly,such as the powerful instance segmentation benchmark Mask R-CNN,which is expanded by the two-stage detector Faster R-CNN,focusing on network accuracy rather than speed,and once became the benchmark for instance segmentation.The instance segmentation algorithm YOLACT extended by the single-stage detector with high-speed detection fills the gap of the real-time instance segmentation model,and has high research and application value.This paper first classifies the instance segmentation algorithm,then analyzes some representative algorithms and their improved algorithms in depth,and explains the advantages and disadvantages of the related algorithms.Finally,the future development of the instance segmentation method is prospected.