本文選題：霧霾環(huán)境 + 車(chē)牌識(shí)別　； 參考：《鄭州大學(xué)》2017年碩士論文

【摘要】：隨著智能交通系統(tǒng)的發(fā)展,車(chē)牌自動(dòng)識(shí)別技術(shù)越來(lái)越廣泛地應(yīng)用于生活中的各種場(chǎng)景。但是由于現(xiàn)今霧霾天氣的增多,傳統(tǒng)的車(chē)牌識(shí)別算法在霧霾天氣下的準(zhǔn)確率會(huì)大幅度下降,很難滿足人們的需求。這就急需在車(chē)牌自動(dòng)識(shí)別過(guò)程中加入去霧算法,提高霧霾條件下的車(chē)牌自動(dòng)識(shí)別準(zhǔn)確率。本文在車(chē)牌自動(dòng)識(shí)別算法中引入暗原色先驗(yàn)去霧算法,同時(shí)利用指導(dǎo)濾波對(duì)暗原色先驗(yàn)去霧算法中透射率優(yōu)化的方法進(jìn)行改進(jìn),在保證去霧效果的同時(shí)縮短了去霧過(guò)程的時(shí)間,提高了去霧算法的實(shí)時(shí)性。對(duì)去霧后得到的圖像,先進(jìn)行灰度化處理,然后進(jìn)行區(qū)域增強(qiáng),最后利用邊緣檢測(cè)的方法來(lái)確定車(chē)牌的上下邊界。接著,利用基于先驗(yàn)知識(shí)的方法確定車(chē)牌的左右邊界,完成車(chē)牌的定位。對(duì)得到的定位后的車(chē)牌圖像先進(jìn)行二值化處理,再利用垂直投影法,通過(guò)垂直方向的像素累計(jì)圖進(jìn)行字符分割,然后對(duì)分割后的車(chē)牌圖像進(jìn)行歸一化處理,最后將歸一化的圖像轉(zhuǎn)化為粗特征矩陣,以便進(jìn)行車(chē)牌識(shí)別。由于BP神經(jīng)網(wǎng)絡(luò)抗干擾性差,其在識(shí)別去霧后的字符圖像時(shí)準(zhǔn)確率下降,本文選用魯棒性強(qiáng)的徑向基函數(shù)(Radial Basis Function,RBF)神經(jīng)網(wǎng)絡(luò)對(duì)去霧字符圖像進(jìn)行識(shí)別。但RBF神經(jīng)網(wǎng)絡(luò)參數(shù)確定較為復(fù)雜,偶然性大,故選用粒子群優(yōu)化算法(Particle Swarm Optimization,PSO)對(duì)其參數(shù)進(jìn)行優(yōu)化。實(shí)驗(yàn)證明,利用基于粒子群算法優(yōu)化的徑向基函數(shù)(Radial Basis Function Optimized by Particle Swarm Optimization,PSO-RBF)神經(jīng)網(wǎng)絡(luò)對(duì)字符進(jìn)行識(shí)別可以有效提高車(chē)牌識(shí)別的準(zhǔn)確率。大量實(shí)驗(yàn)結(jié)果證實(shí),本文算法可以有效提高霧霾條件下的車(chē)牌識(shí)別準(zhǔn)確率,同時(shí)保證車(chē)牌識(shí)別的實(shí)時(shí)性。
[Abstract]:With the development of intelligent transportation system, license plate recognition technology is more and more widely used in all kinds of scenes in life. However, because of the increasing fog and haze weather, the accuracy of the traditional license plate recognition algorithm will decrease greatly in haze weather. It is difficult to meet the needs of people. In this paper, the dark original color priori fog removal algorithm is introduced in the automatic recognition algorithm of the license plate. At the same time, the method of improving the transmittance optimization of the dark original color prior fog algorithm is improved by using the guiding filtering, and the time of the fog removal process is shortened and the time of the fog removal is shortened, and the time of the fog removal process is shortened. The image of the fog removal is real-time. The image obtained after the fog is gray, then the region is enhanced. Finally, the edge detection method is used to determine the upper and lower boundary of the license plate. Then, the left and right boundary of the license plate is determined by using the prior knowledge, and the location of the license plate is completed. First, the license plate image after the location is first obtained. Two value processing is carried out, and then the vertical projection method is used to divide the characters through the vertical pixel accumulative graph, and then the segmented license plate image is normalized. Finally, the normalized image is converted into a rough feature matrix to carry out the license plate recognition. Because of the poor anti-interference ability of the BP God channel network, the character is identified after the fogging character. The accuracy rate of the symbol is decreased. In this paper, the robust radial basis function (Radial Basis Function, RBF) neural network is used to identify the fog character images. But the parameters of the RBF neural network are more complex and the chance is larger, so the particle swarm optimization (Particle Swarm Optimization, PSO) is selected to optimize the parameters. The experiment proves that the parameters are optimized. Using the radial basis function (Radial Basis Function Optimized by Particle Swarm Optimization, PSO-RBF) neural network for character recognition can effectively improve the accuracy of the license plate recognition. A large number of experimental results confirm that the algorithm can effectively improve the accuracy rate of license plate recognition under the haze condition. The real-time performance of the license plate recognition is guaranteed.

【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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1 張群;霧霾環(huán)境下車(chē)牌圖像預(yù)處理及識(shí)別算法研究[D];鄭州大學(xué);2017年

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本文編號(hào)：1873721