本文選題：車牌識(shí)別 + 卡爾曼濾波�。� 參考：《蘇州大學(xué)》2009年碩士論文

【摘要】： 車牌自動(dòng)識(shí)別系統(tǒng)(License Plate Recognition System)是智能交通系統(tǒng)中的重要組成部分,它有著廣泛用途和良好的應(yīng)用前景。目前已有不少研究者投身這一研究領(lǐng)域,并作了一些富有成效的工作。 本文研究了車牌識(shí)別系統(tǒng)的四項(xiàng)關(guān)鍵技術(shù)及其相應(yīng)的實(shí)現(xiàn)方法:車牌圖像預(yù)處理技術(shù)、車牌圖像定位技術(shù)、車牌字符提取技術(shù)、車牌字符識(shí)別技術(shù)。 在車牌圖像預(yù)處理技術(shù)中,針對(duì)陰雨天情況車牌的處理,首先采用分段灰度線性拉伸與卡爾曼濾波相結(jié)合的車牌圖像的去噪方法,實(shí)驗(yàn)結(jié)果表明,該方法可以有效地濾除圖像的白噪聲。 在車牌圖像預(yù)處理基礎(chǔ)上對(duì)車牌定位和分割,本文采用基于數(shù)學(xué)形態(tài)學(xué)和連通域標(biāo)記的車牌圖像定位方法。首先采用9×1的縱向結(jié)構(gòu)元素對(duì)車牌圖像進(jìn)行腐蝕,去除一些噪聲點(diǎn),在此基礎(chǔ)上,根據(jù)車牌字符的長(zhǎng)寬比,采用19×17的結(jié)構(gòu)元素對(duì)圖像進(jìn)行閉運(yùn)算,使得車牌所在的連通域與其他可能與之粘連的相對(duì)獨(dú)立的連通域分開,并基于子圖像進(jìn)行連通域判斷的字符提取。 采用改進(jìn)的BP人工神經(jīng)網(wǎng)絡(luò)的字符識(shí)別方法來設(shè)計(jì)車牌識(shí)別系統(tǒng)。BP網(wǎng)絡(luò)結(jié)構(gòu)為輸入層128個(gè)結(jié)點(diǎn),輸出層結(jié)點(diǎn)有6個(gè),隱含層18個(gè)結(jié)點(diǎn)。在這個(gè)網(wǎng)絡(luò)結(jié)構(gòu)上字母和數(shù)字的識(shí)別精度分別為91.85%、96%。 本文在Visual C++6.0開發(fā)平臺(tái)上,對(duì)以上方法進(jìn)行了開發(fā)和實(shí)現(xiàn),構(gòu)建了一個(gè)車牌自動(dòng)識(shí)別系統(tǒng)。 最后,本文對(duì)車牌自動(dòng)識(shí)別系統(tǒng)的進(jìn)一步發(fā)展方向提出了自己的看法。
[Abstract]:License Plate recognition system (Licensing Plate recognition system) is an important part of Intelligent Transportation system (its). It has a wide range of applications and good application prospects. At present, many researchers have devoted themselves to this research field and done some fruitful work. This paper studies four key technologies of license plate recognition system and their corresponding implementation methods: license plate image preprocessing technology, license plate image location technology, license plate character extraction technology, license plate character recognition technology. In the pre-processing technology of license plate image, aiming at the processing of license plate in rainy and cloudy weather, the method of de-noising the license plate image based on segmented gray linear stretch and Kalman filter is adopted. The experimental results show that, This method can effectively filter the white noise of the image. Based on the preprocessing of license plate image, this paper adopts the method of license plate image location based on mathematical morphology and connected domain marking. First, 9 脳 1 longitudinal structural elements are used to corrode the license plate image and some noise points are removed. Based on this, according to the aspect ratio of the license plate characters, the 19 脳 17 structure element is used to close the image. The connected domain in which the license plate is located is separated from other relatively independent connected domains with which the license plate is located, and the characters of the connected domain judgement are extracted based on the sub-image. The improved character recognition method of BP artificial neural network is used to design the vehicle license plate recognition system. The BP network structure is 128 nodes in the input layer, 6 nodes in the output layer and 18 nodes in the hidden layer. The recognition accuracy of letters and numbers on this network structure is 91.85 / 96, respectively. In this paper, the above methods are developed and implemented on the platform of Visual C 6.0, and a license plate automatic recognition system is constructed. Finally, this paper puts forward my own views on the further development of license plate automatic recognition system.
【學(xué)位授予單位】：蘇州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2009
【分類號(hào)】：TP391.41

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前3條

1 劉厚東;車輛牌照自動(dòng)識(shí)別設(shè)計(jì)與實(shí)現(xiàn)[D];電子科技大學(xué);2011年

2 孫凌紅;集裝箱箱號(hào)智能識(shí)別算法研究[D];武漢理工大學(xué);2012年

3 崔良義;停車場(chǎng)車牌識(shí)別技術(shù)研究與實(shí)現(xiàn)[D];湖南大學(xué);2011年

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本文編號(hào)：2054093