基于深度学习的图像语义分割技术研究综述

Survey of Image Semantic Segmentation Based on Deep Learning

图像语义分割技术是智能系统理解自然场景的关键技术之一,作为视觉智能领域的重要研究方向,该技术在移动机器人、无人机、智能驾驶以及智慧安防等领域具有广阔的应用前景。对于图像语义分割技术的研究发展历程进行了详细评述,包括从传统的语义分割方法到当前主流的基于深度学习的图像语义分割理论及其方法,重点阐述了基于深度学习的图像语义分割技术的框架及其实现过程,进而对当前具有代表性的典型算法的效果以及优缺点进行了分析,然后归纳了算法评价指标,最后对该技术的发展进行了总结与展望。该研究对于从事图像语义分割技术的研究人员和工程技术人员均具有很好的参考意义。

Image semantic segmentation technology is one of the key technologies for intelligent systems to understand natural scenes.As an important research direction in the field of visual intelligence,this technology has broad application prospects in the fields of mobile robots,drones,intelligent driving and smart security.This paper gives a detailed review on the research and development of image semantic segmentation technology,including the traditional semantic segmentation method and the current mainstream image semantic segmentation theory based on deep learning,and the method of image semantic segmentation based on deep learning.It describes the framework and its implementation process,analyzes the effects,advantages and disadvantages of the typical representative algorithms,and then summarizes the algorithm evaluation indicators.Finally,the development of the technology is summarized and forecasted.The paper has a good reference for researchers and engineers who are engaged in image semantic segmentation technology.