本文選題：霧霾環(huán)境 + 車牌識別�。� 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,車牌自動識別技術(shù)越來越廣泛地應(yīng)用于生活中的各種場景。但是由于現(xiàn)今霧霾天氣的增多,傳統(tǒng)的車牌識別算法在霧霾天氣下的準確率會大幅度下降,很難滿足人們的需求。這就急需在車牌自動識別過程中加入去霧算法,提高霧霾條件下的車牌自動識別準確率。本文在車牌自動識別算法中引入暗原色先驗去霧算法,同時利用指導(dǎo)濾波對暗原色先驗去霧算法中透射率優(yōu)化的方法進行改進,在保證去霧效果的同時縮短了去霧過程的時間,提高了去霧算法的實時性。對去霧后得到的圖像,先進行灰度化處理,然后進行區(qū)域增強,最后利用邊緣檢測的方法來確定車牌的上下邊界。接著,利用基于先驗知識的方法確定車牌的左右邊界,完成車牌的定位。對得到的定位后的車牌圖像先進行二值化處理,再利用垂直投影法,通過垂直方向的像素累計圖進行字符分割,然后對分割后的車牌圖像進行歸一化處理,最后將歸一化的圖像轉(zhuǎn)化為粗特征矩陣,以便進行車牌識別。由于BP神經(jīng)網(wǎng)絡(luò)抗干擾性差,其在識別去霧后的字符圖像時準確率下降,本文選用魯棒性強的徑向基函數(shù)(Radial Basis Function,RBF)神經(jīng)網(wǎng)絡(luò)對去霧字符圖像進行識別。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)對其參數(shù)進行優(yōu)化。實驗證明,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)對字符進行識別可以有效提高車牌識別的準確率。大量實驗結(jié)果證實,本文算法可以有效提高霧霾條件下的車牌識別準確率,同時保證車牌識別的實時性。
[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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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

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本文編號：1873721