發(fā)布時(shí)間：2018-08-20 14:48

【摘要】：隨著計(jì)算機(jī)技術(shù)的飛速發(fā)展,在計(jì)算機(jī)視覺(jué)領(lǐng)域內(nèi)的圖像處理和模式識(shí)別等技術(shù)也逐漸變得更加完善,其中檢測(cè)問(wèn)題也是圖像處理等領(lǐng)域大多數(shù)學(xué)者研究的主要問(wèn)題之一,并且在實(shí)際生活中,檢測(cè)問(wèn)題也有著十分廣泛的實(shí)際應(yīng)用。在農(nóng)業(yè)生產(chǎn)領(lǐng)域中,谷類(lèi)作物是我國(guó)糧食十分重要的來(lái)源之一,而玉米作為主要的谷類(lèi)作物,玉米早期的幼苗能夠健康大量的生長(zhǎng)對(duì)我國(guó)糧食安全以及工業(yè)生產(chǎn)都起到非常不可小視的作用。所以能在玉米生長(zhǎng)的初期準(zhǔn)確高效的去除雜草是非常必要的。目前為止人們普遍使用傳統(tǒng)的除草方式,例如人工除草和除草劑除草等等。這些傳統(tǒng)的方法雖然可能會(huì)有很高的準(zhǔn)確率,但是會(huì)消耗很大的人力,這就無(wú)形中提高了人工成本。并且如果大量噴灑化學(xué)除草劑,不僅會(huì)對(duì)食用該谷物的人的健康造成威脅,也會(huì)對(duì)環(huán)境產(chǎn)生嚴(yán)重的污染。同時(shí)如果長(zhǎng)時(shí)間的利用除草劑進(jìn)行除草,也逐漸會(huì)使土壤對(duì)于除草劑有較強(qiáng)的依賴(lài)性,因此除草劑也不是持續(xù)性的除草方案。所以找到一種快速便捷的除草方法是十分必要的�；谏鲜鎏岢龅膯�(wèn)題,本文從計(jì)算機(jī)視覺(jué)以及深度學(xué)習(xí)中領(lǐng)域出發(fā),以能夠快速有效的辨識(shí)玉米幼苗與雜草為目標(biāo)。旨在分析和探索能夠運(yùn)用計(jì)算機(jī)視覺(jué)領(lǐng)域內(nèi)的知識(shí)來(lái)解決自動(dòng)辨識(shí)玉米的方法。本文運(yùn)用了計(jì)算機(jī)視覺(jué)領(lǐng)域中處理檢測(cè)問(wèn)題的相關(guān)知識(shí),提出了能夠快速辨識(shí)玉米幼苗與雜草的方法,并通過(guò)在自己的數(shù)據(jù)集中進(jìn)行了大量實(shí)驗(yàn),對(duì)提出的算法是否可行進(jìn)行論證。本文首先分析了傳統(tǒng)除草方法存在的一些缺點(diǎn),以及智能除草對(duì)于精確度以及處理速度有著較高的要求,之后通過(guò)在溫室中拍攝大量的玉米幼苗與雜草的圖片來(lái)構(gòu)成數(shù)據(jù)集。通過(guò)觀察玉米幼苗與雜草的主要區(qū)別以及聯(lián)想計(jì)算機(jī)視覺(jué)領(lǐng)域檢測(cè)問(wèn)題的一些處理方法,首先通過(guò)對(duì)所采集到的數(shù)據(jù)集進(jìn)行預(yù)處理工作,去除光照以及噪聲的影響,之后分別采用兩種方向來(lái)實(shí)現(xiàn)早期玉米苗與雜草的自動(dòng)辨識(shí)。首先本文采取傳統(tǒng)的人工手動(dòng)選取特征的方法,通過(guò)觀察早期玉米幼苗與雜草的主要區(qū)別來(lái)相應(yīng)的選擇特征。并且根據(jù)不同的特征的特點(diǎn)選取兩種特征分別對(duì)樣本進(jìn)行特征提取,之后將兩種特征點(diǎn)進(jìn)行融合,提取特征向量并且使用分類(lèi)器訓(xùn)練提取到的特征向量,最終得到可以區(qū)分玉米幼苗與雜草的分類(lèi)模型。第二種方法借助深度學(xué)習(xí)中卷積神經(jīng)網(wǎng)絡(luò)可以分類(lèi)的特點(diǎn),基于目前比較流行的Faster R-CNN檢測(cè)模型,借助區(qū)域建議網(wǎng)絡(luò)RPN以及用于分類(lèi)的Fast R-CNN檢測(cè)器,通過(guò)對(duì)自己采集的數(shù)據(jù)集進(jìn)行人工標(biāo)注,調(diào)整網(wǎng)絡(luò)的結(jié)構(gòu)和參數(shù),訓(xùn)練自己的數(shù)據(jù)集,最終得到可以用于分類(lèi)的模型,實(shí)現(xiàn)早期幼苗與雜草的分類(lèi)。整個(gè)過(guò)程我們從實(shí)際問(wèn)題需要出發(fā)。利用計(jì)算機(jī)視覺(jué)領(lǐng)域的知識(shí)為依據(jù),提出了解決問(wèn)題的方法,實(shí)現(xiàn)了早期幼苗與雜草的自動(dòng)辨識(shí)。最后,我們對(duì)所有的論文的內(nèi)容進(jìn)行了概括,提出目前所做工作需要改進(jìn)的地方與此同時(shí)指出將來(lái)需要研究的內(nèi)容。
[Abstract]:With the rapid development of computer technology, image processing and pattern recognition technology in the field of computer vision are becoming more and more perfect. Detection problem is also one of the main problems studied by most scholars in the field of image processing, and in real life, detection problem has a very wide range of practical applications in agriculture. In the field of industrial production, cereal crops are one of the most important sources of grain in China. As a major cereal crop, maize seedlings can grow healthily and massively in the early stage, which plays a very important role in food security and industrial production in China. It's necessary. So far traditional weeding methods, such as artificial weeding and herbicide weeding, are widely used. Although these traditional methods may have high accuracy, they will consume a lot of manpower, which will invisibly increase the cost of labor. And if a large number of chemical herbicides are sprayed, not only will they be used for food. At the same time, if long-term use of herbicides for weeding, the soil will gradually become more dependent on herbicides, so herbicides are not a sustainable weeding program. Therefore, it is necessary to find a fast and convenient weeding method. In order to solve the above problems, this paper starts from the fields of computer vision and in-depth learning, aiming at identifying maize seedlings and weeds quickly and effectively. This paper presents a method to identify maize seedlings and weeds quickly, and demonstrates the feasibility of the proposed algorithm through a large number of experiments in our own data set. Firstly, this paper analyzes some shortcomings of traditional weeding methods, and intelligent weeding has a higher precision and processing speed. High requirements, then by taking a large number of images of maize seedlings and weeds in the greenhouse to form a data set. First of all, the traditional manual feature selection method is adopted to select the characteristics of early maize seedlings and weeds by observing the main differences between early maize seedlings and weeds. After feature extraction, the two feature points are fused to extract feature vectors and trained by classifiers. Finally, a classification model which can distinguish maize seedlings from weeds is obtained. The second method uses convolution neural network in depth learning to classify, which is based on the popular Faster. R-CNN detection model, with the help of RPN and Fast R-CNN detector for classification, labels the collected data sets manually, adjusts the structure and parameters of the network, trains the data sets, and finally gets the model that can be used to classify the early seedlings and weeds. Based on the knowledge of computer vision, this paper presents a method to solve the problem and realizes the automatic identification of early seedlings and weeds. Finally, we summarize the contents of all the papers, and point out what needs to be improved and what needs to be studied in the future.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

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本文編號(hào)：2194022