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農(nóng)業(yè)車輛視覺(jué)實(shí)際導(dǎo)航環(huán)境識(shí)別與分
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and Classification for Vision Navigation Application Environment of Agricultural Vehicle
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    摘要:

    分析了對(duì)路徑識(shí)別影響較大的變光照環(huán)境、雜草環(huán)境和陰影環(huán)境對(duì)農(nóng)業(yè)車輛導(dǎo)航路徑的影響，提出一種實(shí)際環(huán)境中的農(nóng)業(yè)車輛視覺(jué)導(dǎo)航研究方法，即先采用神經(jīng)網(wǎng)絡(luò)算法對(duì)農(nóng)田環(huán)境進(jìn)行自動(dòng)分類，然后再相應(yīng)的選擇不同的路徑識(shí)別方法進(jìn)行處理。環(huán)境識(shí)別與分類試驗(yàn)結(jié)果證明，該方法能夠提高農(nóng)業(yè)車輛視覺(jué)導(dǎo)航系統(tǒng)的實(shí)用性和可靠性，導(dǎo)航環(huán)境的分類準(zhǔn)確率為95%，單幅圖像平均耗時(shí)

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    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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趙博,王猛,毛恩榮,張小超,宋正河.農(nóng)業(yè)車輛視覺(jué)實(shí)際導(dǎo)航環(huán)境識(shí)別與分[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),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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