Runway detection and tracking for unmanned aerial vehicle based on an improved canny edge detection algorithm

Xiaobing Wang*, Baokui Li, Qingbo Geng

*Corresponding author for this work

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

8 Citations (Scopus)

Abstract

A new method based on an improved canny edge detection algorithm for the runway detection and tracking was presented in this paper. Though the traditional Canny Operator has high edge detection capability, it still can be interfered by grave image noise, as its detection accuracy cannot reach single pixel. This paper presented one method that combined Canny Operator with mean filter to represent the runway edge accurately. Then Hough Transform and Chain-Code was used for runway tracking. Experimental results show that this method can have better detection and tracking effect, besides the processing speed is also improved, that laid a favorable foundation for UAV visual navigation.

Original languageEnglish
Title of host publicationProceedings of the 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2012
Pages149-152
Number of pages4
DOIs
Publication statusPublished - 2012
Event2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2012 - Nanchang, Jiangxi, China
Duration: 26 Aug 201227 Aug 2012

Publication series

NameProceedings of the 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2012
Volume2

Conference

Conference2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2012
Country/TerritoryChina
CityNanchang, Jiangxi
Period26/08/1227/08/12

Keywords

  • Canny opertator
  • Image processing
  • Runway detection and travking
  • UAV
  • Visual navigation

Fingerprint

Dive into the research topics of 'Runway detection and tracking for unmanned aerial vehicle based on an improved canny edge detection algorithm'. Together they form a unique fingerprint.

Cite this