復(fù)雜背景下車牌識別算法的研究與實現(xiàn)
發(fā)布時間：2018-12-21 12:14
【摘要】：隨著我國經(jīng)濟水平的不斷上升,機動車保有量逐年提高,更多地服務(wù)于我們的生活。機動車輛的增多對道路使用、機動車管理提出了更高的要求。機動車號牌與車輛之間存在一對一的關(guān)系,因此通過車牌的識別可以更加有效地管理車輛。車牌識別技術(shù)研究近年來一直是智能交通系統(tǒng)研究的熱門之一,但是目前市場上的車牌識別產(chǎn)品,其通用性不足,即在復(fù)雜環(huán)境如光照變化強烈、傾斜、污損、噪聲等情況下車牌識別的效率不高。因此,研究復(fù)雜背景下的車牌識別技術(shù)有著重大的市場價值�；谏鲜霰尘�,本文對車牌識別技術(shù)中的關(guān)鍵環(huán)節(jié)進行了深入的研究,主要包括車牌定位、傾斜校正、字符分割及字符識別等,結(jié)合現(xiàn)代計算機視覺的最新技術(shù)成果,對傳統(tǒng)算法的缺點提出改進或提出新的算法,同時結(jié)合實驗進行驗證分析,取得了良好的效果。本文的主要內(nèi)容如下:1、車牌定位算法。根據(jù)車牌區(qū)域顏色信息和邊緣信息豐富的特點,算法先對邊緣信息進行檢測,再使用HSI空間的顏色分量對邊緣信息進行篩選,接著使用自定義的邊緣連接方法和形態(tài)學(xué)方法填補邊緣信息,再使用連通域和車牌的幾何特征選擇車牌的候選區(qū)域。接著對候選區(qū)域進行精確定位處理,包括使用邊緣信息進行傾斜校正、去除上下和左右邊框。最后,使用SVM方法,對精確定位后的車牌提取HOG和LBP特征,進行偽車牌的剔除,從而定位出真的車牌區(qū)域。2、字符分割算法。本文對傳統(tǒng)的兩種算法提出了改進,詳細論述了改進后的兩種算法,即基于多閾值和連通域的字符分割算法、基于字符間距和二值投影的字符分割算法。前者針對連通域提取時無法一次性提取所有字符的特點,設(shè)計多閾值多次提取符合條件的字符區(qū)域,并提出了非連通漢字的提取方法。后者利用車牌字符間距的特點,找出車牌中第二個字符和第三個字符的分界點,然后使用二值投影和字符的幾何特征進行字符分割。3、字符識別算法。引入卷積神經(jīng)網(wǎng)絡(luò),針對漢字和字母數(shù)字的不同特征,設(shè)計不同的網(wǎng)絡(luò)結(jié)構(gòu),從而對字符進行識別,同時該算法其魯棒性好,提升了應(yīng)對復(fù)雜環(huán)境的能力。本文的算法在VS2010平臺上實現(xiàn),編程語言C++,使用了計算機視覺工具庫OpenCV 1.0。
[Abstract]:With the development of economy in our country, the quantity of motor vehicle has been increasing year by year and serving our life more. The increase of motor vehicles puts forward higher requirements for road use and vehicle management. There is a one-to-one relationship between vehicle license plate and vehicle, so vehicle can be managed more effectively by license plate recognition. In recent years, the research of license plate recognition technology has been one of the hot topics in the research of intelligent transportation system. However, at present, the license plate recognition products in the market are not universal enough, that is, in complex environments, such as intense changes of illumination, tilt, fouling, etc. The efficiency of license plate recognition is not high under the condition of noise and so on. Therefore, the research of license plate recognition technology in complex background has great market value. Based on the above background, this paper has carried on the thorough research to the license plate recognition technology key link, mainly includes the license plate localization, the tilt correction, the character segmentation and the character recognition and so on, unifies the modern computer vision newest technology achievement, An improvement or a new algorithm is proposed for the shortcomings of the traditional algorithm, and good results are obtained by combining the experimental results with the verification and analysis. The main contents of this paper are as follows: 1. License plate location algorithm. According to the rich color information and edge information of license plate region, the algorithm first detects the edge information, and then uses the color component of the HSI space to filter the edge information. Then the self-defined edge linking method and morphological method are used to fill the edge information, and then the connected domain and the geometric feature of the license plate are used to select the candidate region of the license plate. Then the candidate regions are accurately located, including edge information for tilt correction, removal of upper and lower edges and left and right borders. Finally, using the SVM method, the HOG and LBP features are extracted from the accurately located license plate, and the pseudo-license plate is removed, and then the true license plate region. 2. 2, character segmentation algorithm is obtained. In this paper, the two traditional algorithms are improved, and the two improved algorithms are discussed in detail, that is, the character segmentation algorithm based on multi-threshold and connected domain, the character segmentation algorithm based on character spacing and binary projection. In view of the feature that all characters can not be extracted at one time when the connected domain is extracted, the former designs multiple thresholds to extract the character regions that meet the requirements, and proposes a method for extracting disconnected Chinese characters. The latter uses the character spacing of license plate to find out the boundary point between the second character and the third character in the license plate, and then uses binary projection and the geometric feature of the character to segment the character. 3, character recognition algorithm. The convolution neural network is introduced to design different network structures for the different characteristics of Chinese characters and alphanumeric characters so as to recognize characters. At the same time the algorithm has good robustness and improves the ability to deal with complex environments. The algorithm of this paper is implemented on the VS2010 platform, programming language C, using the computer vision tool library OpenCV 1.0.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U495;TP391.41
[Abstract]:With the development of economy in our country, the quantity of motor vehicle has been increasing year by year and serving our life more. The increase of motor vehicles puts forward higher requirements for road use and vehicle management. There is a one-to-one relationship between vehicle license plate and vehicle, so vehicle can be managed more effectively by license plate recognition. In recent years, the research of license plate recognition technology has been one of the hot topics in the research of intelligent transportation system. However, at present, the license plate recognition products in the market are not universal enough, that is, in complex environments, such as intense changes of illumination, tilt, fouling, etc. The efficiency of license plate recognition is not high under the condition of noise and so on. Therefore, the research of license plate recognition technology in complex background has great market value. Based on the above background, this paper has carried on the thorough research to the license plate recognition technology key link, mainly includes the license plate localization, the tilt correction, the character segmentation and the character recognition and so on, unifies the modern computer vision newest technology achievement, An improvement or a new algorithm is proposed for the shortcomings of the traditional algorithm, and good results are obtained by combining the experimental results with the verification and analysis. The main contents of this paper are as follows: 1. License plate location algorithm. According to the rich color information and edge information of license plate region, the algorithm first detects the edge information, and then uses the color component of the HSI space to filter the edge information. Then the self-defined edge linking method and morphological method are used to fill the edge information, and then the connected domain and the geometric feature of the license plate are used to select the candidate region of the license plate. Then the candidate regions are accurately located, including edge information for tilt correction, removal of upper and lower edges and left and right borders. Finally, using the SVM method, the HOG and LBP features are extracted from the accurately located license plate, and the pseudo-license plate is removed, and then the true license plate region. 2. 2, character segmentation algorithm is obtained. In this paper, the two traditional algorithms are improved, and the two improved algorithms are discussed in detail, that is, the character segmentation algorithm based on multi-threshold and connected domain, the character segmentation algorithm based on character spacing and binary projection. In view of the feature that all characters can not be extracted at one time when the connected domain is extracted, the former designs multiple thresholds to extract the character regions that meet the requirements, and proposes a method for extracting disconnected Chinese characters. The latter uses the character spacing of license plate to find out the boundary point between the second character and the third character in the license plate, and then uses binary projection and the geometric feature of the character to segment the character. 3, character recognition algorithm. The convolution neural network is introduced to design different network structures for the different characteristics of Chinese characters and alphanumeric characters so as to recognize characters. At the same time the algorithm has good robustness and improves the ability to deal with complex environments. The algorithm of this paper is implemented on the VS2010 platform, programming language C, using the computer vision tool library OpenCV 1.0.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U495;TP391.41
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