[1]GUO Wenqiang,GAO Xiaoguang,HOU Yongyan,et al.Environment recognition of intelligent agricultural vehicles based on MSBN and multi-agent coordinative inference[J].CAAI Transactions on Intelligent Systems,2013,8(5):453-458.[doi:10.3969/j.issn.1673-4785.201210057]
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Environment recognition of intelligent agricultural vehicles based on MSBN and multi-agent coordinative inference

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 8 Number of periods: 2013 5 Page number: 453-458 Column: 学术论文—机器感知与模式识别 Public date: 2013-10-25
Title:
Environment recognition of intelligent agricultural vehicles based on MSBN and multi-agent coordinative inference
Author(s):
GUO Wenqiang1;  GAO Xiaoguang2;  HOU Yongyan1;  ZHOU Qiang1
1.College of Electrical and Information Engineering, Shaanxi University of Science and Technology, Xi′an 710021, China; 2.School of Electronics and Information, Northwestern Polytechnical University, Xi′an 710129, China
Keywords:
intelligent agricultural vehicle;  multiply sectioned Bayesian network (MSBN);  multi-agent;  coordinative inference;  environment recognition
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201210057
Abstract:
In order to solve the problem existing in the agricultural environment recognition of intelligent vehicles, due to the difficulty of conducting quantitative analysis of the reliability of such recognition, a target recognition algorithm for multi-agent cooperative inference based on the multiply sectioned Bayesian network (MSBN) has been proposed. This method characterizes local information of the multi-agent image acquiring system with MSBN model. In the circumstance of incomplete observations, although each single agent may only capture some local observation information from the target, the message propagation among subnets can be achieved by information update in the overlapping subdomains. By combining the local inference and global inference of reliability communication between subnets in MSBN, the multi-source information was merged to enhance recognition performance. By comparing the traditional neural network and BN method, experimental results illustrate that, the target recognition algorithm based on MSBN can effectively supplement multi-source information, and thus, can improve the recognition accuracy of agricultural vehicles in the complicated environment.
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