本文選題：車牌識(shí)別 + 深度學(xué)習(xí)�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著人們生活節(jié)奏的不斷加快,汽車的普及率越來越高,但是伴隨機(jī)動(dòng)車輛的大幅度增多,道路積壓、城市擁堵、管理復(fù)雜等問題也變得更加突出。智能交通系統(tǒng)可以高效分配資源,提高道路通行能力和交通管理效率,從而緩解城市交通堵塞的壓力。車牌識(shí)別技術(shù)作為智能交通系統(tǒng)的核心技術(shù)之一,是圖像處理、計(jì)算機(jī)視覺、模式識(shí)別和機(jī)器學(xué)習(xí)等多個(gè)領(lǐng)域的交叉融合,廣泛應(yīng)用于交通監(jiān)控、停車場(chǎng)管理、高速公路智能收費(fèi)、電子警察等場(chǎng)景中。實(shí)際上,車牌識(shí)別技術(shù)還可以被嵌入到手持收費(fèi)機(jī)、行車記錄儀,甚至是手機(jī)等移動(dòng)終端中,使智能交通更便利、更貼近生活,但由于采集設(shè)備不固定等原因,造成車牌在圖像中出現(xiàn)的位置、大小隨機(jī),對(duì)車牌識(shí)別技術(shù)提出了更高的要求,因此,對(duì)復(fù)雜場(chǎng)景下的中國(guó)車牌識(shí)別系統(tǒng)的深入研究具有非常重要的現(xiàn)實(shí)意義。本文重點(diǎn)對(duì)復(fù)雜場(chǎng)景下中國(guó)車牌的定位、校正、分割和識(shí)別問題進(jìn)行研究,并提出一些行之有效的改進(jìn)措施。針對(duì)復(fù)雜場(chǎng)景下圖像存在的光照不同、背景復(fù)雜等問題,本文提出基于卷積神經(jīng)網(wǎng)絡(luò)的車牌定位方法,并提出利用多顏色空間的車牌定位方法�；诰矸e神經(jīng)網(wǎng)絡(luò)的定位方法通過不斷訓(xùn)練學(xué)習(xí)網(wǎng)絡(luò)實(shí)現(xiàn)定位的高精準(zhǔn)度,基于多顏色空間的定位方法通過提取多種顏色信息提高車牌定位的精度和效率。針對(duì)拍攝角度、采集設(shè)備晃動(dòng)等因素可能造成車牌傾斜的問題,本文采取基于最長(zhǎng)直線的車牌校正方法,在字符分割之前校正車牌能夠降低分割的難度。針對(duì)道路顛簸、背景噪聲等因素可能造成字符模糊、邊框粘連的問題,本文改進(jìn)基于投影圖像的分割方法,采用設(shè)置閾值的方式進(jìn)行分割,可以提高字符分割的準(zhǔn)確度和實(shí)用性。針對(duì)旋轉(zhuǎn)、形變或模糊的車牌字符難以被準(zhǔn)確識(shí)別的問題,本文提出基于長(zhǎng)度特征的字符識(shí)別方法,通過提取字符的輪廓信息,可以簡(jiǎn)化車牌字符的識(shí)別過程,提高字符的識(shí)別率。
[Abstract]:With the continuous acceleration of people's life rhythm, the popularity rate of cars is getting higher and higher, but with the increasing number of motor vehicles, the problems of road backlog, urban congestion and complex management have become more prominent. Intelligent transportation system can efficiently allocate resources, improve road traffic capacity and traffic management efficiency, thus alleviating urban traffic congestion. As one of the core technologies of intelligent transportation system, the license plate recognition technology is one of the key technologies of the intelligent transportation system. It is a cross fusion of many fields, such as image processing, computer vision, pattern recognition and machine learning. It is widely used in traffic monitoring, parking management, intelligent toll collection of freeway, electric police and so on. In fact, license plate recognition technology can also be used. In the mobile terminals, such as handheld toll machines, recorder and even mobile phones, intelligent traffic is more convenient and closer to life. But because of the unfixed acquisition equipment, the location of the license plate in the image, the random size, and the higher requirements for the license plate recognition technology are put forward. Therefore, the Chinese license plate under the complex scene is given. The in-depth study of recognition system is of great practical significance. This paper focuses on the research of the location, correction, segmentation and recognition of Chinese license plate in complex scenes, and puts forward some effective improvement measures. In this paper, we propose a convolution nerve based on the problems of different illumination and complex background in the complex scene. The method of license plate location in the network, and the method of license plate location using multi color space. The positioning method based on the convolution neural network realizes the high precision of positioning through continuous training learning network. The location method based on multi color space can improve the accuracy and efficiency of the license plate location by extracting a variety of color information. This paper adopts the longest straight line license plate correction method, which can reduce the difficulty of the segmentation before the character segmentation. In view of the road bump, the background noise and other factors may cause the character blurred and the border conglutination. This paper improves the projection image based on the problem. The method of segmentation can improve the accuracy and practicability of the character segmentation. In this paper, the character recognition method based on the length feature is proposed for the problem that the characters of the license plate are difficult to be identified accurately. Improve the recognition rate of characters.

【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U495;TP391.41

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本文編號(hào)：1846550