【摘要】：隨著21世紀(jì)智能交通和信息技術(shù)的發(fā)展,計(jì)算機(jī)輔助人們處理交通問(wèn)題成為了科學(xué)家不斷研究的方向。我國(guó)交通流量隨著汽車(chē)銷(xiāo)量增長(zhǎng)而日益增大,通過(guò)交警使用傳統(tǒng)方法處理道路事故和違章已經(jīng)變得不切實(shí)際。智能交通技術(shù)通過(guò)引入先信息技術(shù)、控制技術(shù)和計(jì)算機(jī)技術(shù),形成一套先進(jìn)的智能交通系統(tǒng)。目前,諸如公交GPS控制系統(tǒng)、車(chē)輛追蹤系統(tǒng)、車(chē)輛信息管理系統(tǒng)、ETC不停車(chē)電子收費(fèi)系統(tǒng)等等,都屬于當(dāng)前智能交通子系統(tǒng)的應(yīng)用。 而作為車(chē)輛信息管理系統(tǒng)中的關(guān)鍵一環(huán),為了有效、快速的判別車(chē)輛身份,車(chē)牌識(shí)別系統(tǒng)成為了研究者不斷改革創(chuàng)新的一個(gè)部分。車(chē)牌識(shí)別系統(tǒng)主要依靠計(jì)算機(jī)圖形處理技術(shù)、模式識(shí)別技術(shù)、智能計(jì)算技術(shù),將車(chē)牌圖片從視頻流得到的圖片中提取出來(lái),并依次進(jìn)行字符的分割與識(shí)別。 車(chē)牌識(shí)別技術(shù)至今依然存在許多困難,如車(chē)牌抓取、車(chē)牌去噪、字符識(shí)別、系統(tǒng)性能要求等等困難。在一個(gè)標(biāo)準(zhǔn)4米高卡口上抓取的1080P圖片中,車(chē)牌部分僅僅占有約120*35像素大小。并且道路卡口照片中存在著大量自然背景和多車(chē)輛等等干擾因素。車(chē)牌圖片噪聲很多,包括過(guò)曝光電、柳丁、車(chē)牌邊框、車(chē)牌污損等等噪聲影響。字符識(shí)別成功率更是由上面兩步成功率和預(yù)處理效果以及識(shí)別方法所決定,并且系統(tǒng)對(duì)實(shí)時(shí)性和準(zhǔn)確性都有明確的要求。 本文主要通過(guò)對(duì)國(guó)內(nèi)外最近方法研究,通過(guò)選取合適的車(chē)牌提取、字符分割、字符識(shí)別算法,在原始方法上加以改進(jìn),使得識(shí)別時(shí)間平均在300ms以?xún)?nèi),數(shù)字及英文字符正確率在95%以上,車(chē)牌識(shí)別率在80%左右。 論文主要進(jìn)行了以下方面的研究與改進(jìn)： (1)車(chē)牌提取方面：在Sobel算子垂直方向邊緣檢測(cè)后運(yùn)用縱向噪聲與橫向噪聲消除方法去除大部分車(chē)體噪聲和環(huán)境噪聲,運(yùn)用改進(jìn)型的二值圖像快速矩化算法將候選車(chē)牌位置標(biāo)識(shí)出,運(yùn)用車(chē)牌矩形特征和顏色特征得到正確的車(chē)牌位置。 (2)字符分割方面：在灰度拉伸、二值化得到較為清晰的車(chē)牌圖片后,運(yùn)用字符高度逼近方法去除大部分車(chē)牌的上下邊框和柳丁,在運(yùn)用簡(jiǎn)化投影特征和車(chē)牌模板特征,以及采用一定容錯(cuò)算法將字符正確分割。 (3)字符識(shí)別方面：通過(guò)對(duì)比兩種字符圖片處理和特征提取方法,最終選擇粗網(wǎng)格特征與投影特征提取方法進(jìn)行字符特征提取。通過(guò)采用兩個(gè)結(jié)構(gòu)簡(jiǎn)單的神經(jīng)網(wǎng)絡(luò),分別用來(lái)識(shí)別漢字,英文與數(shù)字,并且通過(guò)易混淆字符判別神經(jīng)網(wǎng)絡(luò)區(qū)分易混淆字符。這種方法可以提高字符識(shí)別率與字符識(shí)別速度。
[Abstract]:With the development of intelligent transportation and information technology in the 21st century, computer aided people to deal with traffic problems has become the research direction of scientists. The traffic flow in China is increasing with the increase of car sales. It has become impractical to deal with road accidents and violations by traffic police. Intelligent transportation technology forms an advanced intelligent transportation system by introducing information technology, control technology and computer technology. At present, such as bus GPS control system, vehicle tracking system, vehicle information management system, ETC non-stop electronic toll system and so on, all belong to the current application of intelligent transportation subsystem. As a key part of the vehicle information management system, in order to identify the vehicle effectively and quickly, the license plate recognition system has become a part of the innovation and innovation of the researchers. The license plate recognition system mainly relies on computer graphics processing technology pattern recognition technology and intelligent computing technology to extract license plate images from images obtained from video stream and to segment and recognize characters in turn. There are still many difficulties in license plate recognition technology, such as license plate capture, license plate denoising, character recognition, system performance requirements and so on. In a 1080P image captured on a standard 4 m high bayonet, the license plate is only about 120 pixels in size. And there are a lot of interference factors such as natural background and multiple vehicles in the road bayonet photos. License plate images have a lot of noise, including overexposure, Liuding, license plate frame, license plate fouling and other noise effects. The success rate of character recognition is determined by the success rate of the two steps above, the effect of preprocessing and the method of recognition, and the system has clear requirements for real-time and accuracy. This paper mainly through the domestic and foreign recent method research, through the selection suitable license plate extraction, the character segmentation, the character recognition algorithm, in the original method carries on the improvement, causes the recognition time to average within 300ms, The correct rate of digital and English characters is more than 95%, and the recognition rate of license plate is about 80%. The main research and improvements in this paper are as follows: (1) license plate extraction: after the vertical edge detection of Sobel operator, most of the car body noise and environment noise are removed by using longitudinal noise and transverse noise elimination method. The candidate license plate position is identified by the improved binary image fast moment algorithm, and the correct license plate position is obtained by using the rectangular and color features of the license plate. (2) character segmentation: after grayscale stretching and binarization to get clear license plate images, the method of character height approximation is used to remove the upper and lower frames and Liuding of most license plates, and the simplified projection features and license plate template features are used. And a certain fault-tolerant algorithm is used to segment the characters correctly. (3) character recognition: by comparing two methods of character image processing and feature extraction, the coarse mesh feature and projection feature extraction method are selected for character feature extraction. Two neural networks with simple structure are used to recognize Chinese characters, English characters and numbers, and to distinguish easily confused characters by neural networks. This method can improve character recognition rate and character recognition speed.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：U495;TP391.41

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