Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (19): 57-69.DOI: 10.3778/j.issn.1002-8331.2105-0423

Previous Articles     Next Articles

Review on Semantic Segmentation of UAV Aerial Images

CHENG Qing, FAN Man, LI Yandong, ZHAO Yuan, LI Chenglong   

  1. College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichuan 618307, China
  • Online:2021-10-01 Published:2021-09-29



  1. 中国民用航空飞行学院 空中交通管理学院,四川 广汉 618307


With the rapid development of Unmanned Aerial Vehicle(UAV) technology, research institutions and industries have attached importance of UAV’s application. Optical images and videos are vital for the UAV to sense the environment, occupying an important position in UAV vision. As a hot spot of the current research of computer vision, semantic segmentation is widely investigated in the fields of unmanned driving and intelligent robot. Semantic segmentation of UAV aerial images is based on the UAV aerial image semantic segmentation technology to enable the UAV to work in complex scenes. First of all, a brief introduction to the semantic segmentation technology and the application development of UAV is given. Meanwhile, the relevant UAV aerial data sets, characteristics of aerial images and commonly used evaluation metrics for semantic segmentation are introduced. Secondly, according to the characteristics of UAV aerial images, it introduces the relevant semantic segmentation methods. In this section, analysis and comparison are made in three aspects including the small object detection, the real-time performance of the models and the multi-scale information integration. Finally, the related applications of semantic segmentation for UAV are reviewed, including line detection, the application of agriculture and building extraction, and analysis of the development trend and challenges in the future is made.

Key words: Unmanned Aerial Vehicle(UAV) imagery, semantic segmentation, computer vision, deep learning, convolution neural network



关键词: 无人机影像, 语义分割, 计算机视觉, 深度学习, 卷积神经网络