本文關(guān)鍵詞：復(fù)雜背景下的DataMatrix二維碼識(shí)別算法研究　出處：《深圳大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： DataMatrix碼 工業(yè)二維碼 提取研究 算法研究

【摘要】：在工業(yè)領(lǐng)域,利用二維碼對(duì)工業(yè)產(chǎn)品及零部件進(jìn)行標(biāo)識(shí),實(shí)現(xiàn)對(duì)產(chǎn)品及零部件的生成追蹤、裝配管理、生命周期維護(hù)等已經(jīng)成為自動(dòng)化工業(yè)的行業(yè)標(biāo)準(zhǔn)。其中Data Matrix(DM)二維碼因其優(yōu)秀的數(shù)據(jù)壓縮能力和強(qiáng)大的糾錯(cuò)能力受到工業(yè)及物流行業(yè)的青睞。與食品、藥品和其他消費(fèi)類(lèi)產(chǎn)品的包裝不同,工業(yè)二維碼的應(yīng)用環(huán)境通常比較惡劣,二維碼的識(shí)別通常伴隨著噪聲、過(guò)曝、磨損、污染等問(wèn)題。尤其是對(duì)直接部件表示DPM(Direct Part Marking)的識(shí)別,常規(guī)的二維碼識(shí)別方法無(wú)法滿足其需求。因此針對(duì)復(fù)雜背景的DM碼提取算法具有重要意義和迫切的市場(chǎng)需求。基于上述研究背景和需求,分析了目前工業(yè)二維碼的技術(shù)背景和應(yīng)用方向,對(duì)工業(yè)產(chǎn)品及零部件上的DM碼開(kāi)展深入研究,提出了一種基于角度和邊緣信息的DM碼快速定位和自適應(yīng)網(wǎng)格劃分的DM碼采樣方法,實(shí)現(xiàn)在復(fù)雜背景下的DM碼快速準(zhǔn)確識(shí)別。本文的主要研究?jī)?nèi)容包括以下幾個(gè)方面:1.根據(jù)二維碼特征,提出了一種基于角點(diǎn)密度和邊緣信息相結(jié)合的DM二維碼精確定位的方法,包括4部分:基于角點(diǎn)分布的DM碼候選區(qū)域快速定位和優(yōu)先級(jí)排序;DM碼候選區(qū)外輪廓提取,減少非感興趣邊緣對(duì)定位產(chǎn)生干擾;改進(jìn)Hough變換粗定位L邊,快速進(jìn)行直線投票;迭代加權(quán)最小二乘法直線擬合,精確定位L邊。本方法相比傳統(tǒng)的Hough變換定位“L”形邊方法大幅提高了復(fù)雜背景下DM碼區(qū)域定位的精度、速度和魯棒性;2.通過(guò)分析DM碼的特征,提出了基于圖像梯度投影累計(jì)的自適應(yīng)采樣網(wǎng)格劃分算法。通過(guò)對(duì)DM圖像求取梯度,并在其垂直方向上投影累計(jì),將二維圖像映射成一維信號(hào),再對(duì)該信號(hào)進(jìn)行峰-谷提取,波谷即為采樣線位置。最后根據(jù)峰谷分布對(duì)異常采樣線進(jìn)行校正,從而實(shí)現(xiàn)對(duì)DM碼的自適應(yīng)采樣網(wǎng)格劃分。實(shí)驗(yàn)結(jié)果表明,該方法可有效地對(duì)被污染或磨損的DM碼進(jìn)行自適應(yīng)網(wǎng)格劃分,提高惡劣環(huán)境下DM碼的識(shí)別魯棒性。3.基于嵌入式DSP TMS320DM648搭建DM識(shí)別測(cè)試系統(tǒng),以驗(yàn)證本文所提出方法的性能。實(shí)驗(yàn)結(jié)果表明,所提出的方法對(duì)金屬材質(zhì)不同加工工藝的DPM碼均具有良好的識(shí)別能力,可適應(yīng)光照不均、低照度、污損等工業(yè)復(fù)雜環(huán)境下的快速、準(zhǔn)確讀碼,滿足工業(yè)DM識(shí)別需求。
[Abstract]:In the field of industry, we use the QR code to mark the industrial products and parts, and realize the generation tracking and assembly management of the products and parts. Life cycle maintenance has become the industry standard for automation industry. QR code is favored by industry and logistics industry because of its excellent data compression ability and powerful error-correcting ability. The packaging of drugs and other consumer products is different. The application environment of industrial QR codes is usually bad. The recognition of QR codes is usually accompanied by noise, overexposure, wear and tear. Problems such as pollution, especially the identification of direct components representing DPM(Direct Part marking. The conventional two-dimension code recognition method can not meet its needs. Therefore, the DM code extraction algorithm for complex background is of great significance and urgent market demand, based on the above research background and requirements. The technical background and application direction of industrial QR codes are analyzed, and the DM codes on industrial products and parts are studied deeply. In this paper, a method of DM code sampling based on angle and edge information is proposed, which is based on fast location of DM code and adaptive trellis division. In this paper, the main research contents include the following aspects: 1. According to the features of two-dimensional code. In this paper, a method of DM two-dimension code precise location based on corner density and edge information is proposed, which includes four parts: fast location and priority ranking for candidate region of DM code based on corner distribution; DM code candidate region contour extraction, reduce the non-interested edge to the location of interference; The improved Hough transform is used to locate the L-edge and to vote in a straight line quickly. The iterative weighted least square method is used to locate the L edge accurately. Compared with the traditional Hough transform method, the accuracy of the region location of DM code in complex background is greatly improved by this method. Speed and robustness; 2. By analyzing the characteristics of DM code, an adaptive sampling mesh generation algorithm based on image gradient projection cumulation is proposed. The gradient of DM image is obtained and the cumulation is projected in its vertical direction. The 2-D image is mapped to one-dimensional signal, and then the peak to valley is extracted, which is the position of the sampling line. Finally, the abnormal sampling line is corrected according to the peak-valley distribution. The experimental results show that the proposed method is effective for adaptive mesh division of DM codes which are contaminated or worn out. Improve the recognition robustness of DM code in bad environment. 3. Build DM recognition test system based on embedded DSP TMS320DM648. In order to verify the performance of the proposed method, the experimental results show that the proposed method has good recognition ability for DPM codes of different processing processes of metal materials, and can adapt to uneven illumination and low illumination. Fast and accurate code reading in complex environment, such as fouling, can meet the requirement of DM recognition.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.44

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