Field scene road guidance technology based on graph reasoning model and intelligent optimization

Field scene road guidance technology based on graph reasoning model and intelligent optimization
Hua Xia1
Wang Xinqing1
Ma Zhaoye1
Wang Dong1,2
Shao Faming1
1. PLA Army Engineering University, Nanjing 210007, China
2. Second Institute of Engineering Research & Design, Southern Theatre Command, Kunming 650222, China

摘要

In order to realize automatic, universal and accurate identification and guidance of unstructured roads for unmanned equipment in the field environment, this paper proposed a road guidance algorithm for field scenes based on graph reasoning model and intelligent optimization. Firstly, the method segmented the image into homogeneous superpixel blocks, and fused multi-features of the superpixel blocks to construct a training set. Improved the traditional Laplace support vector machine algorithm, combined the location information of superpixel blocks, dynamically selected superpixel seed blocks in road areas, and trained multi-class classifier regressors of superpixel blocks and consistency regressors of adjacent superpixels. Combined the regression values of two kinds of regressors, constructed the energy function of Markov random field and then used the standard graph cutting algorithm to iteratively obtain the minimized energy function to realize the initial road reasoning segmentation. Combined the initial segmentation results of roads, the objective function was constructed according to the constraints set by people′s intuitive perception of roads, and used the differential immune clonal evolution algorithm to intelligently optimize and extract the guide lines of roads. The data collected in Zhushan, Nanjing and DARPA grand challenge database were tested, and it compared the results qualitatively and quantitatively with those of classical algorithms. The results show that the extraction accuracy of the guide line of unstructured roads by this algorithm in the field environment is over 91.79%, compared with classical algorithms, the detection accuracy is increased by 48.1% and 35.5% respectively, and the processing efficiency of the algorithm is increased by 98.6 % and 97.8 % respectively, which balances the real-time performance and accuracy of detection and has a strong application prospect.

基金项目

国家重点研发计划资助项目
国家自然科学基金资助项目
江苏省自然科学基金资助项目
中国博士后科学基金第62批面上资助项目

出版信息

DOI: 10.19734/j.issn.1001-3695.2018.04.0332
出版期卷: 《计算机应用研究》 Printed Article, 2019年第36卷 第10期
所属栏目: Technology of Graphic & Image
出版页码: 3168-3173
文章编号: 1001-3695(2019)10-065-3168-06

发布历史

[2019-10-05] Printed Article

引用本文

华夏, 王新晴, 马昭烨, 等. 基于图推模型与智能寻优的野外道路导向技术 [J]. 计算机应用研究, 2019, 36 (10): 3168-3173. (Hua Xia, Wang Xinqing, Ma Zhaoye, et al. Field scene road guidance technology based on graph reasoning model and intelligent optimization [J]. Application Research of Computers, 2019, 36 (10): 3168-3173. )

关于期刊

  • 计算机应用研究 月刊
  • Application Research of Computers
  • 刊号 ISSN 1001-3695
    CN  51-1196/TP

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