本文選題：二維碼檢測　切入點(diǎn)：通用場景　出處：《浙江工商大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著互聯(lián)網(wǎng)的發(fā)展以及互聯(lián)網(wǎng)應(yīng)用場景的不斷擴(kuò)張,二維碼的使用頻次以及使用領(lǐng)域也在飛速的擴(kuò)大,在目前的支付、社交等領(lǐng)域二維碼技術(shù)已經(jīng)成為了一個不可或缺的技術(shù)。雖然目前二維碼技術(shù)已經(jīng)在上述領(lǐng)域中得到了較好的發(fā)展與普及,但是通用場景下的二維碼檢測算法仍然沒有非常好的效果,因此,限制了二維碼在某些特定領(lǐng)域下的使用。本文研究是一種通用場景下的二維碼檢測系統(tǒng),適用于增強(qiáng)現(xiàn)實(shí)應(yīng)用的開發(fā)以及輔助機(jī)器人SLAM建圖等,該系統(tǒng)需要二維碼檢測算法對形變、光照、距離等干擾因素有較高的抗干擾能力。針對該類應(yīng)用場景,當(dāng)前產(chǎn)品中采用的二維碼檢測算法主要有如下幾個問題:1.主動性檢測。當(dāng)需要對二維碼進(jìn)行檢測的時候,需要用戶主動調(diào)整攝像頭的位置角度使得二維碼成像標(biāo)準(zhǔn)、清晰,才能完成二維碼的檢測識別。2.高度依賴二維碼上的特殊標(biāo)識符。目前現(xiàn)實(shí)場景中通用的二維碼檢測算法都在二維碼生成的時候在特定位置增加了特定標(biāo)志符輔助二維碼檢測。3.由于應(yīng)用場景的限制,單次二維碼的檢測識別只能針對單個二維碼進(jìn)行。因此,上述二維碼檢測算法不適用本文課題研究的二維碼檢測系統(tǒng)應(yīng)用的場景。為了解決上述問題,本文設(shè)計了一種基于卷積神經(jīng)網(wǎng)絡(luò)算法的通用場景下的二維碼檢測系統(tǒng),該系統(tǒng)可以對當(dāng)前攝像頭所處的視野范圍內(nèi)的二維碼進(jìn)行快速準(zhǔn)確的識別,適用于通用的場景下,同時具備姿態(tài)估計能力,可以對當(dāng)前的檢測到的二維碼進(jìn)行姿態(tài)估計,使得系統(tǒng)能在二維碼位置進(jìn)行三維模型重建等功能,該系統(tǒng)適用于本文上文所述的領(lǐng)域。根據(jù)實(shí)際測試結(jié)果,本文設(shè)計的基于二維碼檢測算法在PC上已經(jīng)可以接近實(shí)時性,同時根據(jù)對本文的驗(yàn)證數(shù)據(jù)集的測試,檢測精度達(dá)到了 97.7%。
[Abstract]:With the continuous expansion of the development of the Internet and Internet application scenarios, the frequency of use and the use of two-dimensional code field in the rapid expansion in the current payment, social and other fields of two-dimensional code technology has become an indispensable technology. Although the two-dimensional code technology has been in the field achieved good development and popularization however, the detection algorithm of two-dimensional code general scenario is still not very good results, therefore, limits the use of two-dimensional code in some specific fields. This paper is a two-dimensional code under general scene detection system, to enhance the development of practical applications and robot assisted SLAM mapping, the system needs to detection algorithm of two-dimensional code on deformation, light, anti interference ability of interference factors distance is high. According to the application scenarios, the products used in the two-dimensional code detection method Mainly has the following problems: 1. initiative detection. When the need for the detection time of the two-dimensional code, the user need to take the initiative to adjust the camera position angle makes the two-dimensional code imaging standard, clear, to complete the detection and identification of the.2. code is highly dependent on the special standard two-dimensional code identifier. When the detection algorithm of two-dimensional code universal reality in the scene are generated in the two-dimensional code in a specific position increases the specific detection of.3. marker assisted two-dimensional code due to the application of scene constraints, detection and identification of only single two-dimensional code for a single two-dimensional code. Therefore, the application of detection system of two-dimensional code detection algorithm of the two-dimensional code is not applicable to the subject of this thesis to the scene. To solve the above problems, this paper designs a detection system of two-dimensional code general scene algorithm of convolutional neural network based on the can of the current camera system The two-dimensional code view within the scope of the fast and accurate identification, suitable for general scene, at the same time with the attitude estimation ability, the two-dimensional code can be detected on the attitude estimation, the system could function in three-dimensional model reconstruction in the two-dimensional code, the system is applicable to herein above. According to the actual test results, this paper designs the detection algorithm of two-dimensional code is almost real-time based on the PC, at the same time according to the verification of the data in the test, the detection accuracy reached 97.7%.

【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP183;TP391.44;TP274

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 曹琳;高v，

本文編號：1645544