Detection of dangerous water area during UAV autonomous landing

Shaoshan Liu, Jianmei Song, Haoping She*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Aiming at the problem of water dangerous area detection faced by UAV during emergency autonomous landing, the features of water dangerous area are extracted from the image by neural network, the texture features of the image are obtained by HOG algorithm, and the features extracted by neural network and texture features are classified by support vector machine method (SVM). Then, the classifier is trained based on color features and regional texture features to detect the specific location of water hazard areas in the image. The experiment shows that the method has a good result in detecting the dangerous area of water during UAV autonomous landing, and the detection accuracy can reach more than 90%.

Original languageEnglish
Title of host publicationProceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4609-4615
Number of pages7
ISBN (Electronic)9798350334722
DOIs
Publication statusPublished - 2023
Event35th Chinese Control and Decision Conference, CCDC 2023 - Yichang, China
Duration: 20 May 202322 May 2023

Publication series

NameProceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

Conference

Conference35th Chinese Control and Decision Conference, CCDC 2023
Country/TerritoryChina
CityYichang
Period20/05/2322/05/23

Keywords

  • UAV
  • neural network
  • support vector machine
  • water detection

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