农业车辆视觉实际导航环境识别与分
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and Classification for Vision Navigation Application Environment of Agricultural Vehicle
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    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

    Abstract:

    23ms。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 23ms. 

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赵博,王猛,毛恩荣,张小超,宋正河.农业车辆视觉实际导航环境识别与分[J].农业机械学报,2009,40(7):166-170. and Classification for Vision Navigation Application Environment of Agricultural Vehicle[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(7):166-170.

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