期刊文献+

基于机器视觉的工程机械行驶速度的研究 被引量:2

Machine-vision-based study on driving speed of construction machinery
下载PDF
导出
摘要 工程机械行驶速度是预测机器的牵引力、滑转损失、牵引效率和滑转率等牵引性能参数时所需要的基本变量,传统的接触式测量方法由于行驶路面条件等因素的影响,所测量的速度值并非是工程机械的实际行驶速度.利用工程机械行驶路面的纹理随机性,提出了基于机器视觉技术的工程机械行驶速度测量方法.该方法通过安装在车体上的CCD摄像机,在连续成像的条件下,通过匹配跟踪得到路面同一纹理特征在相邻两帧图像中的位置,利用摄像机内外参数和成像帧频实时计算得到工程机械的瞬时行驶速度.通过采集路面纹理图像进行模拟实验验证了该方法的可行性. The driving speed of construction machinery is one of the basic variables to predict such traction property parameters as traction force, slipping loss, traction efficiency and slipping rate. Owing to driving surface conditions, the measured speeds from traditional contact measurement methods are not equivalent to actual driving speeds. Based on the textural randomness of driving surfaces, a machine- vision-based technology is first proposed for driving speed measurement. By matched-tracking the consecutive images via the CCD camera installed on vehicle body, the position in two neighboring frames of the same textural property is then obtained. With interior and exterior parameters and image frame frequencies, the real-time instantaneous driving speeds are calculated. Finally, the feasibility of the proposed method is verified via experimental simulation by capturing surface texture images.
出处 《中国工程机械学报》 2012年第4期446-451,共6页 Chinese Journal of Construction Machinery
基金 道路施工技术与装备教育部重点实验室开放基金资助项目 中央高校基本科研业务费专项基金资助项目(CHD2010ZY011)
关键词 工程机械 机器视觉 速度测量 匹配 construction machinery machine vision speed measurement matching
  • 相关文献

参考文献8

二级参考文献26

共引文献34

同被引文献27

  • 1杨龙,刘焱雄,周兴华,唐秋华,丁继胜.GPS测速精度分析与应用[J].海洋测绘,2007,27(2):26-29. 被引量:17
  • 2王新成.PAL全电视信号的特征与处理技术[J].电视技术,1997(5):16-31. 被引量:5
  • 3Liu L, Du W T. The vehicle-borne electronic image stabilizationsystem based on gray projection algorithm[C] // Proc. of the In-ternational Conference on Electric Information and Control En-gineering ,2011 : 4687 - 4690.
  • 4Jin T, Chen B,Zhou Z. Image-domain estimation of wall para-meters for autofocusing of through-the-wall SAR imagery [J].IEEE Trans, on Geoscience and Remote Sensing , 2013, 51 (3):1836 - 1843.
  • 5Naeem S,Siraj S. A framework to select edge detection methodusing multi-criteria decision making[C3 // Proc. of the IEEE Interna-tional Conference on Systems,Man and Cybernetics .2013 : 730 - 735.
  • 6Zhang K,Li X,Zhang J. A robust point-matching algorithm forremote sensing image registration[J]. IEEE Geoscience and Re-mote Sensing Letters11(2) : 469 - 473.
  • 7Chalom E, Asa E, Biton E. Measuring image similarity: anoverview of some useful applicationsCJ1 . IEEE Instrumenta-tion &. Measurement Magazine ?2013 ,12(2) : 24 - 28.
  • 8Burget R, Rai J K,Uher V,et al. Supervised video scene seg-mentation using similarity measures[C]//Proc. of the Interna-tional Gonference on Telecommunications and Signal Proces-sing y 2013: 793 - 797.
  • 9Mahyari A G,Yazdi M. Panchromatic and muhispectral imagefusion based on maximization of both spectral and spatial simi-larities[J]. IEEE Trans, on Geoscience and Remote Sensing ,2011,49(6): 976 - 985.
  • 10Hector F, Gomez G,Marroquin L. Image registration, based, onkernel-predictability[J]. Computer Vision and Image Under-standing ,2008, 112(2) ; 160 - 172.

引证文献2

二级引证文献9

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部