【摘要】：農(nóng)業(yè)裝備智能化自主導(dǎo)航技術(shù)能夠促進(jìn)和提高農(nóng)業(yè)機(jī)械化生產(chǎn)效率。新疆是我國(guó)紅棗主產(chǎn)區(qū)之一,主要采用矮化密植種植模式,為視覺(jué)導(dǎo)航的應(yīng)用提供了有利的應(yīng)用條件。本文以矮化密植棗園田管時(shí)期和收獲時(shí)期機(jī)械作業(yè)環(huán)節(jié)為研究對(duì)象,重點(diǎn)探討了田管時(shí)期和收獲時(shí)期兩個(gè)階段的棗園環(huán)境目標(biāo)像素分布特征,目標(biāo)特征離散點(diǎn)群的檢測(cè)算法及視覺(jué)導(dǎo)航路徑的擬合方法。本文主要研究?jī)?nèi)容如下:(1)搭建矮化密植棗園視覺(jué)導(dǎo)航路徑檢測(cè)系統(tǒng)和圖像采集方案。軟件系統(tǒng)由目標(biāo)像素分布規(guī)律分析模塊,圖像預(yù)處理模塊,路徑提取模塊,視頻檢測(cè)模塊構(gòu)成,導(dǎo)航路徑性能參數(shù)提取模塊。硬件系統(tǒng)圖像采集設(shè)備選用佳能IXUS870相機(jī),圖像處理硬件平臺(tái)采用處理器AMD Athlon(tm)II X2 240 Processor,主頻:2.8GHz,內(nèi)存:2GB,系統(tǒng)類型:32位WINDOWS 7操作系統(tǒng)的計(jì)算機(jī)。(2)田管時(shí)期及收獲時(shí)期機(jī)械化生產(chǎn)過(guò)程的視覺(jué)導(dǎo)航候補(bǔ)點(diǎn)群的檢測(cè)算法研究。針對(duì)田管時(shí)期,將圖像分為兩個(gè)小類來(lái)處理,第一類是行間有雜草,施肥和剪枝環(huán)境,第二類環(huán)境為中耕作業(yè)環(huán)境。針對(duì)第一小類,采用|R-B|色差法灰度化,第二類采用傳統(tǒng)灰度化方法,兩類灰度化均采用大津法分割圖像,而后采用梯形掃描算法、面積法、垂直投影等方法去噪。針對(duì)收獲時(shí)期圖像,將圖像歸為晴天、陰天、逆光,順光、背景多元疊加等五個(gè)類型進(jìn)行研究。采用B分量圖像進(jìn)行灰度化,利用基于行掃描自適應(yīng)方法分割目標(biāo)背景,通過(guò)行間掃描方法、灰度垂直投影、形態(tài)學(xué)處理去噪。通過(guò)引入趨勢(shì)線方法描述行間邊緣走勢(shì),采用點(diǎn)到直線的距離公式提取離散點(diǎn)候補(bǔ)點(diǎn)群,結(jié)果證明,提取目標(biāo)點(diǎn)群符合棗樹(shù)行列分布特征。(3)采用最小二乘法對(duì)離散候補(bǔ)點(diǎn)群擬合邊緣線,而后求取兩條邊緣線中線作為視覺(jué)導(dǎo)航路徑。試驗(yàn)表明,本文研究算法具有比較高的準(zhǔn)確率和魯棒性,檢測(cè)到的特征點(diǎn)與各個(gè)時(shí)期的特征分布吻合。在本文搭建的軟件系統(tǒng)下,圖像的寬乘高采用230×168,田管時(shí)期靜態(tài)圖像檢測(cè):平均每張圖像路徑檢測(cè)平均時(shí)間小于14.0s,檢測(cè)準(zhǔn)確率高于78.3%,視頻檢測(cè)準(zhǔn)確率達(dá)80%以上,每幀圖像檢測(cè)平均耗時(shí)低于2.3s;收獲時(shí)期靜態(tài)圖像檢測(cè)單一工況條件下準(zhǔn)確率高于83.4%,每張圖像檢測(cè)平均時(shí)間小于9.2s,多工況條件為45%,每張圖像平均檢測(cè)時(shí)間低于9.4s,視頻檢測(cè)單一工況準(zhǔn)確率達(dá)81.3%以上,每幀檢測(cè)平均耗時(shí)低于1.7s,多工況條件檢測(cè)準(zhǔn)確率為42.3%,每幀平均檢測(cè)耗時(shí)為1.0s。
[Abstract]:Intelligent autonomous navigation technology of agricultural equipment can promote and improve the efficiency of agricultural mechanization production. Xinjiang is one of the main producing areas of red jujube in China, which mainly adopts dwarf and dense planting pattern, which provides favorable conditions for the application of visual navigation. In this paper, the characteristics of pixel distribution of environmental targets in jujube orchard during the two stages of field management and harvest were discussed, taking the mechanical operation links of the dwarf and dense jujube orchard as the research object. The detection algorithm of discrete point group of target features and the fitting method of visual navigation path. The main contents of this paper are as follows: (1) build a vision navigation path detection system and image acquisition scheme for dwarf and dense jujube garden. The software system consists of object pixel distribution analysis module, image preprocessing module, path extraction module, video detection module, navigation path performance parameter extraction module. Canon IXUS870 camera is used for image acquisition in hardware system. The hardware platform of image processing adopts the computer of processor AMD Athlon (tm) II X240 Processor, main frequency: 2.8 GHz, memory of 2 GBs, system type of 32 bit WINDOWS 7 operating system. (2) Research on the detection algorithm of visual navigation alternate point group in mechanized production process in field management period and harvest period. In view of the field management period, the image was divided into two subgroups, the first was the interrow weed, fertilization and pruning environment, and the second was the intercropping environment. For the first small category, R-B color difference method is used to grayscale, the second kind is traditional grayscale method, the two kinds of grayscale method are used to segment image, then trapezoidal scanning algorithm, area method and vertical projection method are used to remove noise. In this paper, the image of harvest period is classified into five types: sunny, overcast, reverse light, shinning and background multivariate superposition. B component image is used to grayscale and line scan adaptive method is used to segment the background of the target. The method of interline scanning gray vertical projection and morphological processing is used to remove noise. The trend line method is introduced to describe the interline edge trend, and the distance formula from point to line is used to extract the alternate point group of discrete points. (3) the least square method is used to fit the edge line of the discrete alternate point group, and then two central lines of edge line are selected as the visual navigation path. The experimental results show that the proposed algorithm has high accuracy and robustness, and the detected feature points are consistent with the feature distribution of each period. In the software system built in this paper, the width multiplication and height of the image is 230 脳 168. The static image detection during the field management period: the average time of each image path detection is less than 14.0s, the detection accuracy is higher than 78.3%, the video detection accuracy is more than 80%. The average time of image detection per frame is less than 2.3 s, the accuracy of static image detection in harvest period is more than 83.4 under a single working condition, the average time of each image detection is less than 9.2 s, the multi-working condition is 45, the average detection time of each image is less than 9.4 s, and the average detection time of each image is less than 9.4 s. The accuracy rate of frequency detection in a single working condition is more than 81.3%. The average detection time per frame is less than 1.7 s, the detection accuracy of multi-working conditions is 42.3 and the average detection time per frame is 1.0 s.
【學(xué)位授予單位】：石河子大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：S665.1;TP391.41

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