摘要
分析了对路径识别影响较大的变光照环境、杂草环境和阴影环境对农业车辆导航路径的影响,提出一种实际环境中的农业车辆视觉导航研究方法,即先采用神经网络算法对农田环境进行自动分类,然后再相应的选择不同的路径识别方法进行处理。环境识别与分类试验结果证明,该方法能够提高农业车辆视觉导航系统的实用性和可靠性,导航环境的分类准确率为95%,单幅图像平均耗时23 ms。
The influence of the various illumination, weed, and shadow environment to the path recognition was investigated. A new research method for vision navigation system of agricultural vehicle in the application environment was proposed. The various navigation environments were classified automatically by neural network. According to classification results, different path recognition methods were applied in navigation system. Experimental results of the recognition and the classification prove that the method is effective and can improve the practicability and reliability of vision navigation system. The classification correct rate is 95 %, and the average cost time is 23 ms.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2009年第7期166-170,共5页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家"863"高技术研究发展计划资助项目(2006AA10A305)
关键词
农业车辆
视觉导航
实际环境
路径识别
神经网络
Agricultural vehicle, Vision navigation, Application environment, Path recognition, Neural network
