本文選題：紙幣面值識(shí)別 + 序列號(hào)識(shí)別　； 參考：《北京郵電大學(xué)》2012年碩士論文

【摘要】：隨著計(jì)算機(jī)技術(shù)的高速發(fā)展,自動(dòng)化、智能化系統(tǒng)在各行業(yè)內(nèi)的應(yīng)用越來(lái)越廣泛。智能紙幣識(shí)別系統(tǒng)可以自動(dòng)完成對(duì)紙幣進(jìn)行鑒偽、按質(zhì)量分類、面值識(shí)別和序列號(hào)識(shí)別等工作,成為銀行業(yè)的一大助力。然而,目前功能強(qiáng)大的紙幣識(shí)別系統(tǒng)往往是大型設(shè)備,體積大,功耗大,造價(jià)也高。因此,可移動(dòng)、低功耗和高性價(jià)比的嵌入式紙幣識(shí)別系統(tǒng)將受到中小企業(yè)的歡迎。本文以此為目標(biāo),研究和設(shè)計(jì)嵌入式紙幣識(shí)別系統(tǒng)。 本文對(duì)現(xiàn)有紙幣識(shí)別算法進(jìn)行了大量的調(diào)整和改進(jìn)：提出了一種全局閾值和局部分布特點(diǎn)相結(jié)合的二值化方法,并采用邊緣圖像二值化和分段二值化等策略對(duì)原算法中多處二值化算法進(jìn)行了有針對(duì)性的調(diào)整,以適應(yīng)紙幣圖像的特點(diǎn)；提出一種新的基于模板匹配的序列號(hào)定位算法,以解決原算法效率低下和準(zhǔn)確率不足的問題；另外,本文對(duì)紙幣邊界定位、圖像噪聲處理和序列號(hào)字符分割等多個(gè)算法進(jìn)行了調(diào)整。經(jīng)過(guò)以上改進(jìn)工作,紙幣定位準(zhǔn)確率由93%提高到100%,序列號(hào)字符分割準(zhǔn)確率也有了很大提高。 本文選擇了性價(jià)比較高的DM642作為嵌入式系統(tǒng)的處理器,并完成了紙幣識(shí)別算法向嵌入式平臺(tái)的移植工作,用C語(yǔ)言重寫了STL中的Vector類和部分OpenCV函數(shù)。為了提高處理速度,本文亦對(duì)系統(tǒng)進(jìn)行了多層次的優(yōu)化工作,包括C代碼優(yōu)化與DSP平臺(tái)相關(guān)優(yōu)化。經(jīng)過(guò)優(yōu)化,本文使面值識(shí)別和序列號(hào)識(shí)別的時(shí)間都從1000ms以上降到20ms以下,達(dá)到實(shí)時(shí)處理的標(biāo)準(zhǔn)。
[Abstract]:With the rapid development of computer technology and automation, intelligent system is more and more widely used in various industries. Intelligent banknote recognition system can automatically complete the identification of banknotes, according to the quality of classification, face value recognition and serial number identification, become a great help of the banking industry. However, the current powerful banknote recognition system is often a large equipment, large volume, high power consumption and high cost. Therefore, mobile, low-power and high-cost-effective embedded banknote recognition system will be welcomed by small and medium-sized enterprises. This paper studies and designs an embedded banknote recognition system. In this paper, a large number of adjustments and improvements have been made to the existing banknote recognition algorithms: a binarization method combining the global threshold and the local distribution characteristics is proposed. In order to adapt to the characteristics of banknote image, a new sequence number location algorithm based on template matching is proposed, which adopts the strategies of edge image binarization and segmented binarization to adjust the binarization algorithm of the original algorithm to suit the characteristics of the paper currency image. In order to solve the problems of low efficiency and low accuracy of the original algorithm, in addition, the paper adjusts several algorithms, such as border location of paper currency, image noise processing and serial number character segmentation. After the above improvement, the accuracy rate of banknote positioning is improved from 93% to 100%, and the accuracy of serial number character segmentation is also greatly improved. In this paper, DM642 with high performance-to-price ratio is chosen as the processor of embedded system, and the paper currency recognition algorithm is transplanted to embedded platform. The Vector class and some OpenCV functions in STL are rewritten with C language. In order to improve the processing speed, this paper also carries on the multi-level optimization work to the system, including the C code optimization and the DSP platform correlation optimization. After optimization, the time of face value recognition and serial number recognition is reduced from above 1000ms to below 20ms, which is up to the standard of real time processing.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.41;TP368.1

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前3條

1 王遙;基于Arm-Linux的周界警戒算法研究及其移植與優(yōu)化[D];北京郵電大學(xué);2013年

2 施虹宇;TMS320 DM642上的代碼優(yōu)化研究[D];北京郵電大學(xué);2013年

3 黃榮斌;嵌入式人臉識(shí)別系統(tǒng)及車牌字符分割算法研究[D];北京郵電大學(xué);2013年

，

本文編號(hào)：1835136