【作者】 徐国保； 尹怡欣； 殷路； 郝彦爽； 周美娟；

【Author】 Xu Guobao1,2,Yin Yixin1,Yin Lu1,Hao Yanshuang1,Zhou Meijuan2 1.School of Information Engineering,University of Sci.&Tech.Beijing,Beijing 100083,P.R.China2.Information School,Guangdong Ocean University,Zhanjiang 524088,P.R.China

【机构】 北京科技大学信息工程学院； 广东海洋大学信息学院；

【摘要】 针对非结构化道路的阴影和水迹等环境因素的影响,利用细胞神经网并行图像处理能力,提出一种基于细胞神经网的道路图像快速分割算法。采用细胞神经网的灰度阈值分割,膨胀腐蚀,边缘检测等操作。实验结果表明:该算法具有较强的环境适应能力,不仅能实现结构化道路和非结构化道路快速分割,而且能消除阴影和水迹对道路图像分割效果的影响,快速有效地分割出道路区域。更多还原

【Abstract】 The main factors that affect segmentation of unstructured road images are shadows and water marks on the road surface.Taking advantage of the parallel image processing capability of cellular neural networks,a fast algorithm for road image segmentation based on cellular neural networks was proposed.In the algorithm,gray threshold segmentation,dilation and erosion,and edge detection using CNN are performed successively.Experimental results demonstrated that the algorithm has strong environmental adaptability,which can fast segment structured and unstructured roads.The proposed method can segment the lane area quickly,effectively and robustly,and can eliminate the influence of shadows and water marks on the segmentation of road images.更多还原