Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (8): 13-27.DOI: 10.3778/j.issn.1002-8331.2210-0144

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review of Real-Time Semantic Segmentation Algorithms for Deep Learning

HE Jiafeng, CHEN Hongwei, LUO Dehan   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 511400, China
  • Online:2023-04-15 Published:2023-04-15



  1. 广东工业大学 信息工程学院,广州 511400

Abstract: Semantic segmentation is a technique to segment different objects in a picture from the perspective of pixels and label each pixel in the original picture. However, due to UAV navigation, remote sensing images, medical diagnosis and other application fields, real-time semantic segmentation is needed. Therefore, the real-time semantic segmentation technology based on deep learning has developed rapidly. There are many technologies and models for real-time semantic segmentation. Based on this, on the basis of studying the related literature, the real-time semantic segmentation technology is introduced by semantic segmentation technology, and the advantages of real-time semantic segmentation are briefly described. Then, the important and difficult points of real-time semantic segmentation are discussed. According to the important and difficult points, the existing related technologies and models are expounded, and the advantages and disadvantages of the technologies and models are summarized. Finally, the challenges faced by real-time semantic segmentation are prospected, and the real-time semantic segmentation is summarized, which provides some theoretical references for the follow-up discussion.

Key words: real-time semantic segmentation, deep learning, computer vision, real-time prediction

摘要: 语义分割是从像素的角度分割出图片中的不同对象,并对原始图片中的每个像素进行标注的一种技术。但由于无人机导航、遥感图像、医疗诊断等应用领域需要实时地进行语义分割处理。所以,基于深度学习的实时语义分割技术得到了迅速的发展。实时语义分割技术发展至今已有许多的技术与模型。基于此,在对相关文献进行研究的基础上,由语义分割技术引出了实时语义分割技术,并简单叙述了实时语义分割的优点。随后,研讨出目前实时语义分割存在的重难点。根据重难点进而对已存在的相关技术与模型进行阐述,并总结技术与模型的优缺点。最后,展望实时语义分割所面临的挑战,并对实时语义分割进行了总结与归纳,为后续的研讨提供了一些理论参考。

关键词: 实时语义分割, 深度学习, 计算机视觉, 实时预测