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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农业车辆视觉实际导航环境识别与分   [下载全文]
and Classification for Vision Navigation Application Environment of Agricultural Vehicle   [Download Pdf][in English]
  
DOI:10.3969/j.issn.1000-1298.[year].[issue].[sequence]
中文关键词:  农业车辆  视觉导航  实际环境  路径识别  神经网络
基金项目:
赵博  王猛  毛恩荣  张小超  宋正河
中国农业机械化科学研究院
中文摘要: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
Key Words:
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. 

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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