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Elevator door state detection method based on improved probability Hough line detection


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Elevator door state detection method based on improved probability Hough line detection
Zhang GuofuWang Cheng
Internet of Things Engineering Institute,Jiangnan University,Wuxi 214122,China
elevator door state detection line detection Hough transform image processing
For the problem of installing external sensors difficultly in the car and high cost,an elevator door state line detection method based on pre-image processing is proposed here,and a corresponding solution to the interference of door background and door panel texture on line detection algorithm is presented. Firstly,it is preprocessed to extract elevator door image from video stream; Secondly,the elevator door feature lines are extracted by the improved probability Hough line detection algorithm; Finally,the real-time detection of elevator door opening,closing and fault status is realized by combining judgment rules based on prior knowledge. The effectiveness of the algorithm is verified by selecting multiple door switch pictures. The experimental results prove that,compared with the probability Hough line detection algorithm,the limited-angle probability Hough line detection algorithm proposed in this paper reduces the time consumption by 10% when extracting the characteristic lines,has the higher precision and the recall rate,and is suitable for the front elevator door state detection system.


[1] 杨华江. 电梯安全隐患的分析及对策探讨[J]. 科技信息,2009(11):48-49.
Yang Huajiang. Elevator safe hidden danger analysis and countermeasure discussion[J]. Science & Technology Information,2009(11):48-49.
[2]金晓磊,潘鹏. 机器人视觉的电梯轿厢门状态识别系统[J]. 单片机与嵌入式系统应用,2018,18(4):28-31.
Jin Xiaolei,Pan Peng. Recognizing system for state of elevator door based on robot vision[J]. Microcontrollers & Embedded Systems,2018,18(4):28-31.
[3]张书,殷勤. 电梯门锁安全回路改进设计[J]. 电子测试,2014(14):41-43.
Zhang Shu,Yin Qin. The improved design of elevator door lock loop[J]. Electronic Test,2014(14):41-43.
[4]张艳,张重阳,郁生阳. 基于框线检测的票据图像分类方法[J]. 南京理工大学学报,2007,31(4):409-413.
Zhang Yan,Zhang Chongyang,Yu Shengyang. Bill image classification method based on line detection[J]. Journal of Nanjing University of Science and Technology,2007,31(4):409-413.
[5]Yoshihiko M. N-point Hough transform for line detection[J]. Journal of Visual Communication & Image Representation,2009,20(4):242-253.
[6]Rahmdel P,Comley R,Shi D,et al. A review of Hough transform and line segment detection approaches[C]//Proceedings of the 10th International Conference on Computer Vision Theory and Applications. Berlin,Germany:SCITEPRESS,2015:411-418.
[7]Matas J,Kittler J,Galambos C. Robust detection of lines using the progressive probabilistic Hough transform[J]. Computer Vision and Image Understanding,2000,78(1):119-137.
[8]吴欣,张志伟. 基于形态学和霍夫变换的文档图像倾斜检测[J]. 南京理工大学学报,2009,33(2):178-182.
Wu Xin,Zhang Zhiwei. Skew detection of document images using mathematical morphology and Hough transform[J]. Journal of Nanjing University of Science and Technology,2009,33(2):178-182.
[9]蒋斌,李超英,李宗谕,等. 基于决策树分类的毫米波雷达对电力线的检测[J]. 南京理工大学学报,2017,41(1):95-99.
Jiang Bin,Li Chaoying,Li Zongyu,et al. Millimeter wave radar detection in power line based on decision tree classification[J]. Journal of Nanjing University of Science and Technology,2017,41(1):95-99.
[10]王越,范先星,刘金城,等. 结构化道路上应用区域划分的车道线识别[J]. 计算机应用,2015,35(9):2687-2691.
Wang Yue,Fan Xianxing,Liu Jincheng,et al. Lane line recognition using region division on structured roads[J]. Journal of Computer Applications,2015,35(9):2687-2691.
[11]Li Yadi,Chen Liguo,Huang Haibo,et al. Nighttime lane markings recognition based on canny detection and Hough transform[C]//Proceedings of IEEE International Conference on Real-time Computing & Robotics. Angkor Wat,Cambodia:IEEE,2016:411-415.
[12]Rafael G G,Jérémie J,Morel J M,et al. LSD:A line segment detector[J]. Image Processing On Line,2012(2):35-55.
[13]李涛,陈黎,聂晖. 基于改进线段分割检测的电线杆遮挡检测算法[J]. 计算机工程,2017,43(9):250-255.
Li Tao,Chen Li,Nie Hui. Utility pole occlusion detection algorithm based on improved line segmentation detection[J]. Computer Engineering,2017,43(9):250-255.
[14]Liu Hong,Qian Yueliang,Lin Shouxun. Detecting persons using Hough circle transform in surveillance video[C]//Proceedings of the Fifth International Conference on Computer Vision Theory and Applications. Angers,France:SCITEPRESS,2010:267-270.
[15]Ahmed S A. Comparative study among Sobel,Prewitt and Canny edge detection operators used in image processing[J]. Journal of Theoretical and Applied Information Technology,2018,96(18):6517-6525.
[16]Yin Xingyun. Operators to dilate and erode color impulse noise images based on the morphology[C]//2011 International Conference on E-business & E-government. Shanghai,China:IEEE,2011:1-3.
[17]Manzanera A,Nguyen T P,Xu Xiaolei. Line and circle detection using dense one-to-one Hough transforms on greyscale images[J]. EURASIP Journal on Image and Video Processing,2016(1):46-64.


Last Update: 2020-04-20