Otsu算法在基于ROS系统的变电站机器人导航线提取中的应用研究
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摘要
针对变电站机器人导航线和路面背景区分度高且图像分布具有规则性的特点,将采集图像预处理后,首先在HSV空间中进行导航线颜色分量抽取,接下来转换到YCbCr空间中利用Cb分量灰度分布对光照强度不敏感的特征,比较扫描图像局部方差和最大类间方差,通过对Otsu算法进行改进,快速完成导航线背景信息的剔除,从而达到提取导航线的目的.该算法应用基于ROS系统的轮式机器人,通过实验表明该算法在不同天气和光照条件下总体有效率可达98.9%,具有较高的鲁棒性.
A novel algorithm is proposed because there is high degree of distinction between navigation-line extracted by transformer substation robot and background area,and the images are distributed in regular ways.The algorithm first extracts the color component of navigation-line from the preprocessed images in the HSV space.Then,in order to quickly remove the background information to extract the navigation-line,the algorithm improves the Otsu algorithm by comparing and scanning the local variance and the maximal between-cluster variance of the image whereas the Cb component's grayscale distribution is insensitive to light intensity in the YCbCr space.The experiment results show that the overall efficiency of the algorithm is up to 98.9% in different weather and light conditions when it is applied to wheel robot based on the ROS system,and that the algorithm has high robustness.
A novel algorithm is proposed because there is high degree of distinction between navigation-line extracted by transformer substation robot and background area,and the images are distributed in regular ways.The algorithm first extracts the color component of navigation-line from the preprocessed images in the HSV space.Then,in order to quickly remove the background information to extract the navigation-line,the algorithm improves the Otsu algorithm by comparing and scanning the local variance and the maximal between-cluster variance of the image whereas the Cb component's grayscale distribution is insensitive to light intensity in the YCbCr space.The experiment results show that the overall efficiency of the algorithm is up to 98.9% in different weather and light conditions when it is applied to wheel robot based on the ROS system,and that the algorithm has high robustness.
引文
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[2]杨森,董吉文,鲁守银.变电站设备巡检机器人视觉导航方法[J].电网技术,2009(03):11-16.
[3]史思总.基于ROS的室内变电站设备巡检机器人定位技术[D].绵阳:西南科技大学,2016.
[4]朱鸽,郑杰肖宇峰.基于UVC协议的ROS视频监控系统设计[J].微型机与应用,2015,(19)14-18.
[5]Chunming Li,Chenyang Xu,et al.Distance regularized level set evolution and its application to image segmentation[J].IEEE transactions on image processing,2010,19(20):3243-3254.
[6]G Dong,M Xie.Color clustering and learing for image segmentation based on neural networks[J].IEEE Transactions on Neural Networks,2007,18(5):1424-1432.
[7]李朝晖,陈明.基于小波和自学习神经网络的图像分割[J].计算机应用研究,2006,23(1):246-249.
[8]EH Said,DEM Nassar,G Fahmy,HH Ammar.Teeth segmentation in digitized dental X-ray films using mathematical morphology[J].IEEE Transactions on Information Forensics and Security,2008:178-189.
[9]纪则轩,潘瑜,陈强等.无监督模糊C-均值聚类自然图像分割方法[J].中国图象图形学报,2011,16(5):773-780.
[10]刘松涛,殷福亮.基于图论的图像分割方法及其新进展[J].自动化学报,2012,38(6):911-922.
[11]Tao wenbing,Interactively multiphase image segmentation based on variational formulation and graph cuts[J].Pattern Recognition,2010,(43):3208-3218.
[12]龚劬,廖武忠,卢力,等.基于图论的快速FCM图像分割算法[J].计算机工程,2012,38(8):192-197.