基于视频数据的机场跑道外来物检测

陈唯实; 李敬

doi:10.13700/j.bh.1001-5965.2013.0762

基于视频数据的机场跑道外来物检测

doi: 10.13700/j.bh.1001-5965.2013.0762

陈唯实,
李敬

中国民航科学技术研究院机场研究所, 北京 100028

基金项目: 民航安全能力建设资金项目(142146903033)

详细信息

作者简介:
陈唯实(1982-),男,天津人,高级工程师,chenwsh@mail.castc.org.cn.

中图分类号: TP751.1
计量
- 文章访问数: 1697
- HTML全文浏览量: 261
- PDF下载量: 688
- 被引次数: 0
出版历程
- 收稿日期: 2014-01-13
- 网络出版日期: 2014-12-20

Foreign object debris detection for airport runway with video data

Airport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing 100028, China

摘要

摘要: 提出了一种基于视频数据的机场跑道外来物(FOD,Foreign Object Debris)检测算法,该算法包括几何校正、背景差分、杂波抑制和伪装消除等4个步骤.其中,几何校正排除了摄像头轻微抖动造成的图像差异;背景差分利用每个像素对应的颜色向量信息,建立背景模型并进行定时更新;杂波抑制和伪装消除利用差分图像和原始图像信息,建立马尔科夫随机场和概率统计模型,在降低虚警的同时提高了检测率.将本算法应用于两组不同光照条件下获取的视频图像,检测不同形状和颜色的FOD目标;采用本算法对机场实测数据进行测试,验证了算法的有效性.
- 视频 /
- 跑道外来物 /
- 检测 /
- 背景差分 /
- 杂波抑制 /
- 马尔科夫随机场
Abstract: Foreign object debris (FOD) detection algorithm was proposed for airport runway with video data, including four steps of geometric adjustment, background subtraction, noise suppression and camouflage elimination. Firstly, geometric adjustment rejected the image change due to slight camera motion. In the step of background subtraction, the color vector information to each pixel was used for the establishment of the background model and its periodical update. For noise suppression and camouflage elimination, Markov random field and probability statistical model were established based on the information from the subtracted and original images, which reduced the false alarms and improved the detection rate. The algorithm was applied to two sets of live video images under different lighting conditions, with the FOD targets of different shapes and colors. The algorithm effectiveness was also approved by the tests on the airport ground-truth data.
- video /
- foreign object debris (FOD) /
- detection /
- background subtraction /
- noise suppression /
- Markov random field

HTML全文

参考文献(16)

[1]	NAS-412 REV.1 Foreign object damage/foreign object debris(FOD) prevention[S]
[2]	Patterson Jr J.Foreign object debris(FOD)detection research[J].International Airport Review,2008,11(2):22-27
[3]	FAA AC:150/5220-24 Airport foreign object debris (FOD) detection equipment[S]
[4]	李煜,肖刚.机场跑道异物检测系统设计与研究[J].激光与红外,2011,41(8):909-915 Li Yu,Xiao Gang.Study and design on FOD detection and surveillance system for airport runway[J].Laser & Infrared,2011,41(8):909-915(in Chinese)
[5]	樊曼劼.机场跑道异物检测研究[D].北京:北京交通大学,2011 Fan Manjie.Airport FOD detection research on runway[D].Beijing:Beijing Jiaotong University,2011(in Chinese)
[6]	Xu Q Y,Ning H S,Chen W S.Video-based foreign object debris detection[C]//Proceedings of IEEE International Workshop on Imaging Systems and Techniques.Piscataway,NJ:IEEE Comput- er Society,2009:123-126
[7]	Beasley P D L,Binns G,Hodges R D,et al.Tarsier®,a millimeter wave radar for airport runway debris detection[C]//Proceedings of European Radar Conference.Amsterdam:Horizon House Publishing Ltd,2004:261-264
[8]	Vogel B.An object lesson in finding FOD[J].Jane's Airport Review,2008,20(8):56-57
[9]	Radke R J,Andra S,Al-Kofahi O,et al.Image change detection algorithm: a systematic survey[J].IEEE Transactions on Image Processing,2005,14(3):294-307
[10]	Zitova B,Flusser J.Image registration methods:a survey[J].Image and Vision Computing,2003,21(11):977-1000
[11]	Haines T S F,Xiang T.Background subtraction with dirichlet processes[C]//Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).Heidelberg:Springer Verlag,2012,7575 LNCS(PART 4):97-111
[12]	Diaz R,Hallman S,Fowlkes C.Detecting dynamic objects with multi-view background subtraction[C]//Proceedings of IEEE International Conference on Computer Vision (ICCV).Sydney:IEEE,2013:273-280
[13]	Shimada A,Nagahara H,Taniguchi R.Backgroundmodeling based on bidirectional analysis[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington D C:IEEE Computer Society,2013:1979-1986
[14]	Elgammal A,Duraiswami R,Harwood D,et al.Background and foreground modeling using nonparametric kernel density estimation for visual surveillance[J].Proceedings of the IEEE,2002,90(7):1151-1162
[15]	Barnich O,Van Droogenbroeck M.ViBe:auniversal background subtraction algorithm for video sequences[J].IEEE Transactions on Image Processing,2011,20(6):1709-1724
[16]	McHugh J,Konrad J,Saligrama V,et al.Foreground-adaptive background subtraction[J].IEEE Signal Processing Letters,2009,16(5):390-393