【摘要】：隨著智能化裝備在工業(yè)生產(chǎn)中的廣泛應(yīng)用,機(jī)器視覺作為智能化裝備的一個(gè)重要分支受到很大重視。而一直以來國(guó)產(chǎn)視覺處理軟件在速度、抗干擾等方面性能表現(xiàn)不佳,盡管很多國(guó)內(nèi)企業(yè)和研究機(jī)構(gòu)在做相關(guān)方面的研究,但國(guó)內(nèi)市場(chǎng)一直被國(guó)外的優(yōu)秀視覺解決方案企業(yè)占領(lǐng)很大的份額。本文研究了一種穩(wěn)定可靠并且高效的視覺識(shí)別和定位算法,成功解決多項(xiàng)工程任務(wù),具體開展了如下幾個(gè)方面的工作:首先,研究了一種基于形狀的模板匹配算法,相似度量采用模板邊緣的梯度向量和目標(biāo)物體邊緣梯度向量作點(diǎn)積來度量。通過采取貪心算法較大地提高的算法的匹配速度,然后通過圖像金字塔降低圖像分辨率的方式,降低了算法的復(fù)雜度。通過兩種速度提升方式,成功地提高了算法匹配速度。并且研究了其它的模板匹配算法,包括基于互相關(guān)系數(shù)法的匹配方法和性能表現(xiàn)優(yōu)異的基于豪斯多夫距離和霍夫變換的模板匹配方法。其次,針對(duì)不同復(fù)雜程度的目標(biāo)物體,提出了自動(dòng)確定其圖像邊緣幅值分割的最低和最高閾值、圖像金字塔的層數(shù)和模板搜索的平移步長(zhǎng)和旋轉(zhuǎn)步長(zhǎng)的算法,使得匹配算法更加智能。匹配過程的實(shí)現(xiàn)中通過對(duì)比試驗(yàn),確定了圖像金字塔建立所需的最佳的濾波方式,解決了最高層金字塔尋優(yōu)的問題。采用C++編程語言在VS 2013開發(fā)環(huán)境下開發(fā)了視覺識(shí)別和定位軟件。隨后,通過設(shè)計(jì)實(shí)驗(yàn)來驗(yàn)證算法的性能。分為兩類實(shí)驗(yàn)分別測(cè)試算法的抗干擾性能和算法識(shí)別所用的時(shí)間兩項(xiàng)性能�？垢蓴_性能測(cè)試包括抗遮擋、混亂和抗非線性光照變化的性能。本文算法通過與互相關(guān)系數(shù)法和基于豪斯多夫距離以及基于霍夫變換的三種匹配算法進(jìn)行對(duì)比,驗(yàn)證了本文基于形狀的模板匹配算法在處理物體部分信息缺失以及抗光線干擾和處理時(shí)間方面的優(yōu)異性能。最后,搭建了Delta機(jī)器人視覺分揀系統(tǒng),完成了相機(jī)與傳送帶之間的標(biāo)定,機(jī)器人與傳送帶之間的位置標(biāo)定,保證分揀系統(tǒng)抓取的準(zhǔn)確性。通過基于圖像序列的動(dòng)態(tài)目標(biāo)跟蹤算法避免了目標(biāo)的多次識(shí)別和抓取。通過自主開發(fā)的視覺處理算法,成功解決了火腿腸自動(dòng)分揀項(xiàng)目中腸體的識(shí)別和定位,并且此項(xiàng)目已經(jīng)成功應(yīng)用于實(shí)際生產(chǎn)中,達(dá)到了高效的分揀任務(wù),大大提高了生產(chǎn)效率。
[Abstract]:With the wide application of intelligent equipment in industrial production, machine vision as an important branch of intelligent equipment has received great attention. And domestic visual processing software has been performing poorly in terms of speed, anti-jamming, and so on, although many domestic enterprises and research institutions are doing related research. But the domestic market has been foreign excellent visual solutions enterprises occupy a large share. In this paper, a stable, reliable and efficient visual recognition and localization algorithm is studied, which successfully solves many engineering tasks. The following works are carried out: firstly, a form-based template matching algorithm is studied. Similarity measures are measured by dot product of gradient vector of template edge and gradient vector of object edge. By adopting greedy algorithm to improve the matching speed of the algorithm, and then reducing the image resolution through the image pyramid, the complexity of the algorithm is reduced. The matching speed of the algorithm is improved successfully by two speed lifting methods. And other template matching algorithms are studied, including the matching method based on the correlation number method and the template matching method based on the Hausdorf distance and Hoff transform with excellent performance. Secondly, an algorithm is proposed to automatically determine the minimum and highest threshold values of image edge amplitude segmentation, the number of layers of image pyramid and the translation step size and rotation step size of template search for different complex objects. It makes the matching algorithm more intelligent. In the realization of the matching process, the optimal filtering method for image pyramid building is determined through the contrast experiment, and the problem of the top pyramid optimization is solved. Visual recognition and location software is developed by using C programming language in vs 2013 development environment. Then, the performance of the algorithm is verified by designing experiments. It is divided into two kinds of experiments to test the performance of anti-jamming and the time of recognition respectively. Anti-jamming performance tests include anti-occlusion, chaos, and anti-nonlinear illumination variations. The algorithm is compared with the correlation number method, the three matching algorithms based on Hausdorf distance and Hoff transform. It is verified that the shape based template matching algorithm has excellent performance in dealing with the missing information of objects and the ability to resist light interference and processing time. Finally, the Delta robot visual sorting system is built, the calibration between camera and conveyor belt is completed, and the position calibration between robot and conveyor belt is completed to ensure the accuracy of the grabbing system. The dynamic target tracking algorithm based on image sequence is used to avoid multiple target recognition and capture. Through the self-developed visual processing algorithm, the identification and location of the midgut body of the ham sausage automatic sorting project has been successfully solved, and this project has been successfully applied in practical production, which has achieved a highly efficient sorting task and greatly improved the production efficiency.
【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
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 洪玲;;PatMax圖像匹配軟件在變速箱裝配中的應(yīng)用[J];智慧工廠;2016年10期

2 倪鶴鵬;劉亞男;張承瑞;王云飛;夏飛虎;邱正師;;基于機(jī)器視覺的Delta機(jī)器人分揀系統(tǒng)算法[J];機(jī)器人;2016年01期

3 呂鐵;韓娜;;智能制造:全球趨勢(shì)與中國(guó)戰(zhàn)略[J];人民論壇·學(xué)術(shù)前沿;2015年11期

4 王影;冷單;;我國(guó)智能制造裝備產(chǎn)業(yè)的現(xiàn)存問題及發(fā)展思路[J];經(jīng)濟(jì)縱橫;2015年01期

5 王田苗;陶永;;我國(guó)工業(yè)機(jī)器人技術(shù)現(xiàn)狀與產(chǎn)業(yè)化發(fā)展戰(zhàn)略[J];機(jī)械工程學(xué)報(bào);2014年09期

6 張國(guó)福;沈洪艷;;機(jī)器視覺技術(shù)在工業(yè)檢測(cè)中的應(yīng)用綜述[J];電子技術(shù)與軟件工程;2013年22期

7 Ju Huo;Wen-Bo Dong;Ning Yang;Wu-Kang Lin;;Calibration of Camera with Large Field-of-View Based on Flexible Planar Target[J];Journal of Harbin Institute of Technology;2013年04期

8 吳曉軍;鄒廣華;;基于邊緣幾何特征的高性能模板匹配算法[J];儀器儀表學(xué)報(bào);2013年07期

9 張紅霞;;國(guó)內(nèi)外工業(yè)機(jī)器人發(fā)展現(xiàn)狀與趨勢(shì)研究[J];電子世界;2013年12期

10 WANG Zuo;XIAO PengFeng;GU XingFa;FENG XueZhi;LI XiaoYing;GAO HaiLiang;LI Hui;LIN JinTang;ZHANG XueLiang;;Uncertainty analysis of cross-calibration for HJ-1 CCD camera[J];Science China(Technological Sciences);2013年03期

相關(guān)博士學(xué)位論文 前3條

1 張朝陽;基于視覺的機(jī)器人廢金屬分揀系統(tǒng)研究[D];中國(guó)農(nóng)業(yè)大學(xué);2015年

2 張文昌;Delta高速并聯(lián)機(jī)器人視覺控制技術(shù)及視覺標(biāo)定技術(shù)研究[D];天津大學(xué);2012年

3 張青林;機(jī)器視覺高速圖像處理平臺(tái)中關(guān)鍵技術(shù)的研究[D];武漢大學(xué);2010年

相關(guān)碩士學(xué)位論文 前8條

1 劉子龍;基于機(jī)器視覺的快速分揀食品包裝系統(tǒng)研究[D];浙江工業(yè)大學(xué);2015年

2 鄧明星;并聯(lián)Delta機(jī)器人的傳送帶動(dòng)態(tài)抓取系統(tǒng)設(shè)計(jì)[D];廣東工業(yè)大學(xué);2014年

3 崔芮;基于金字塔結(jié)構(gòu)的人臉識(shí)別算法研究[D];西安電子科技大學(xué);2014年

4 張俊凱;一種快速的旋轉(zhuǎn)模板匹配算法的設(shè)計(jì)與實(shí)現(xiàn)[D];哈爾濱工業(yè)大學(xué);2013年

5 曹京京;Hausdorff距離的計(jì)算原理及其在二維匹配中的應(yīng)用[D];大連理工大學(xué);2013年

6 唐濤;基于Hausdorff距離的相似性度量方法研究[D];廣西大學(xué);2012年

7 周麗莎;基于模板匹配的視覺定位技術(shù)研究與應(yīng)用[D];大連理工大學(xué);2012年

8 劉錦峰;圖像模板匹配快速算法研究[D];中南大學(xué);2007年

，

本文編號(hào)：2184701