車牌及車標識別技術(shù)的研究
發(fā)布時間：2019-04-02 08:59
【摘要】：車輛識別技術(shù)是智能交通系統(tǒng)(Intelligent Transportation System,ITS)的關(guān)鍵技術(shù)之一,車牌及車標識別技術(shù)是車輛識別技術(shù)的重要研究領(lǐng)域。車牌與車標是車輛信息的重要組成部分,其在車庫管理、路口收費、交通違章抓拍等場景都發(fā)揮了極其重要的作用,具有很大的經(jīng)濟價值和現(xiàn)實意義。本課題對近年來車牌識別及車標識別技術(shù)相關(guān)的理論和算法做了較為全面的了解和分析,系統(tǒng)地闡述了車牌識別技術(shù)和車標識別技術(shù)的難點。在PC平臺上完成車牌識別系統(tǒng)和車標識別系統(tǒng)的開發(fā)和優(yōu)化,其中重點對車牌識別技術(shù)中涉及的字符分割和字符識別、車標識別技術(shù)中涉及的車標定位、識別部分進行深入研究和改進。本文主要工作如下:(1)在車牌字符分割方面,應用了一組基本圖像處理方法對車牌分割前圖像進行預處理,并基于此提出了一種動態(tài)模板結(jié)合非零像素點的字符分割方法。首先根據(jù)車輛牌照字符按比例排列分布的特性設(shè)置車牌模板,使用模板在預處理后的車牌圖像中滑動,并動態(tài)地改變模板的寬度,每滑動一次計算模板中七個字符區(qū)域內(nèi)非零像素點的數(shù)目,以包含最大的非零像素點數(shù)目的模板位置作為最終車牌字符的分割位置,實現(xiàn)字符分割。對實驗數(shù)據(jù)庫進行測試,包含預處理在內(nèi)的字符分割模塊的正確率為95.62%,平均耗時14.75ms。(2)在字符識別方面,實現(xiàn)了基于支持向量機(Support Vector Machine,SVM)結(jié)合局部二值模式(Local Binary Pattern,LBP)特征的字符識別,經(jīng)測試檢驗,該方法有較好的準確率且耗時較少。對識別錯誤的字符進行統(tǒng)計分析,歸類觀察到錯誤字符多為圖像質(zhì)量較差(如傾斜、殘缺、模糊等)的字符�；诖�,本文整理收集了大量低質(zhì)量字符樣本,加入原字符訓練集中重新訓練得到分類器,將字符識別正確率從94.80%提高至97.73%,實驗結(jié)果表明訓練集對樣本的覆蓋程度對分類器的性能有很大影響。(3)在車標定位方面,針對已有車牌檢測技術(shù)實現(xiàn)車標的定位。該定位方法首先對車標周圍水平或豎直紋理進行抑制以突顯車標區(qū)域,然后消除車標周圍的噪聲點,利用矩形結(jié)構(gòu)元素實現(xiàn)車標精確定位。對實驗數(shù)據(jù)庫進行測試,包含車牌定位在內(nèi)的車標定位模塊的準確率為94.19%。(4)在車標識別方面,實現(xiàn)了基于SVM結(jié)合方向梯度直方圖(Histogram ofOriented Gradient)特征的車標識別。對實驗數(shù)據(jù)庫中的車標進行識別,準確率達到了 97.74%,平均耗時3.60ms,滿足實時要求。
[Abstract]:Vehicle recognition technology is one of the key technologies of Intelligent Transportation system (Intelligent Transportation System,ITS). License plate recognition and vehicle mark recognition are important research fields of vehicle recognition technology. License plate and vehicle mark are important parts of vehicle information. They play a very important role in garage management, intersection charging, traffic violation capture and other scenes, which have great economic value and practical significance. This paper makes a comprehensive understanding and analysis of the theories and algorithms related to license plate recognition and vehicle mark recognition technology in recent years, and systematically expounds the difficulties of license plate recognition technology and vehicle mark recognition technology. The development and optimization of the license plate recognition system and the vehicle mark recognition system on the PC platform are completed, in which the character segmentation and character recognition involved in the license plate recognition technology and the car mark location involved in the car mark recognition technology are emphasized. In-depth research and improvement are carried out in the identification section. The main work of this paper is as follows: (1) in the aspect of license plate character segmentation, a group of basic image processing methods are used to pre-process the image before license plate segmentation, and a character segmentation method based on dynamic template combined with non-zero pixel is proposed. Firstly, the license plate template is set according to the character of the vehicle license plate character arranged proportionally, and the template is used to slide in the preprocessed license plate image, and the width of the template is changed dynamically. The number of non-zero pixels in the seven character regions of the template is calculated each time, and the position of the template containing the maximum number of non-zero pixels is used as the segmentation position of the final license plate character to achieve character segmentation. The experiment database is tested. The correct rate of character segmentation module including preprocessing is 95.62%, and the average time consuming is 14.75 Ms. (2) in the aspect of character recognition, the (Support Vector Machine, based on support vector machine is realized. SVM) combined with local binary pattern (Local Binary Pattern,LBP (local binary pattern) character recognition, the test results show that the proposed method has good accuracy and less time-consuming. By statistical analysis of the wrong characters, it is found that most of the wrong characters are the characters with poor image quality (such as slant, incomplete, fuzzy, etc.). Based on this, this paper collects a large number of low-quality character samples, adds the original character training set to re-train to get a classifier, and improves the correct rate of character recognition from 94.80% to 97.73%. The experimental results show that the coverage of the training set has a great impact on the performance of the classifier. (3) in the aspect of vehicle mark location, the vehicle mark location based on the existing license plate detection technology is realized. Firstly, the horizontal or vertical texture around the car mark is suppressed to highlight the area of the car mark, then the noise around the car mark is eliminated, and the precise positioning of the car mark is realized by using the rectangular structure element. The experimental database is tested, and the accuracy of the vehicle mark location module including license plate location is 94.19%. (4) in the aspect of vehicle mark recognition, the vehicle mark recognition based on SVM combined with direction gradient histogram (Histogram ofOriented Gradient) feature is realized. The recognition accuracy is 97.74% and the average time consuming is 3.60ms, which meets the real-time requirement.
【學位授予單位】：廣西師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495;TP391.41
本文編號：2452433
[Abstract]:Vehicle recognition technology is one of the key technologies of Intelligent Transportation system (Intelligent Transportation System,ITS). License plate recognition and vehicle mark recognition are important research fields of vehicle recognition technology. License plate and vehicle mark are important parts of vehicle information. They play a very important role in garage management, intersection charging, traffic violation capture and other scenes, which have great economic value and practical significance. This paper makes a comprehensive understanding and analysis of the theories and algorithms related to license plate recognition and vehicle mark recognition technology in recent years, and systematically expounds the difficulties of license plate recognition technology and vehicle mark recognition technology. The development and optimization of the license plate recognition system and the vehicle mark recognition system on the PC platform are completed, in which the character segmentation and character recognition involved in the license plate recognition technology and the car mark location involved in the car mark recognition technology are emphasized. In-depth research and improvement are carried out in the identification section. The main work of this paper is as follows: (1) in the aspect of license plate character segmentation, a group of basic image processing methods are used to pre-process the image before license plate segmentation, and a character segmentation method based on dynamic template combined with non-zero pixel is proposed. Firstly, the license plate template is set according to the character of the vehicle license plate character arranged proportionally, and the template is used to slide in the preprocessed license plate image, and the width of the template is changed dynamically. The number of non-zero pixels in the seven character regions of the template is calculated each time, and the position of the template containing the maximum number of non-zero pixels is used as the segmentation position of the final license plate character to achieve character segmentation. The experiment database is tested. The correct rate of character segmentation module including preprocessing is 95.62%, and the average time consuming is 14.75 Ms. (2) in the aspect of character recognition, the (Support Vector Machine, based on support vector machine is realized. SVM) combined with local binary pattern (Local Binary Pattern,LBP (local binary pattern) character recognition, the test results show that the proposed method has good accuracy and less time-consuming. By statistical analysis of the wrong characters, it is found that most of the wrong characters are the characters with poor image quality (such as slant, incomplete, fuzzy, etc.). Based on this, this paper collects a large number of low-quality character samples, adds the original character training set to re-train to get a classifier, and improves the correct rate of character recognition from 94.80% to 97.73%. The experimental results show that the coverage of the training set has a great impact on the performance of the classifier. (3) in the aspect of vehicle mark location, the vehicle mark location based on the existing license plate detection technology is realized. Firstly, the horizontal or vertical texture around the car mark is suppressed to highlight the area of the car mark, then the noise around the car mark is eliminated, and the precise positioning of the car mark is realized by using the rectangular structure element. The experimental database is tested, and the accuracy of the vehicle mark location module including license plate location is 94.19%. (4) in the aspect of vehicle mark recognition, the vehicle mark recognition based on SVM combined with direction gradient histogram (Histogram ofOriented Gradient) feature is realized. The recognition accuracy is 97.74% and the average time consuming is 3.60ms, which meets the real-time requirement.
【學位授予單位】：廣西師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U495;TP391.41
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