發(fā)布時間：2018-03-01 15:08

  本文關(guān)鍵詞： 模糊車牌識別 圖像模糊度評價 模糊字符識別 字典學(xué)習(xí) 稀疏表示　出處：《南京郵電大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：作為智能交通系統(tǒng)的重要組成部分之一,車牌識別在人們的日常生活中發(fā)揮著不可替代的作用。但是由于監(jiān)控攝像機自身存在缺陷、惡劣天氣的影響以及車輛與攝像機之間的相對運動等因素,部分采集到的車牌圖像通常會變得模糊,因此就有必要建立能夠識別模糊車牌圖像的應(yīng)用系統(tǒng)。本文通過對模糊車牌圖像的特征進行詳細分析,以及對車牌識別、圖像模糊度評價、模糊圖像識別等技術(shù)進行深入研究,實現(xiàn)了對模糊車牌識別系統(tǒng)的建立。本文的主要工作及研究成果包括以下三個方面:1、總結(jié)了常見的車牌圖像定位、字符分割以及預(yù)處理方法,并結(jié)合需要識別的模糊車牌圖像的特點,提出了適用于模糊車牌圖像的定位、字符分割以及預(yù)處理方法,然后基于以上方法創(chuàng)建了模糊字符圖像庫。2、實現(xiàn)了對模糊車牌圖像模糊度的評價。模糊度評價的目的是將嚴重模糊的車牌圖像以及比較模糊的車牌圖像區(qū)分開,從而只針對比較模糊的車牌圖像設(shè)計識別模型。首先,結(jié)合人眼對模糊車牌后五位字符的識別能力建立了一個模糊度主觀評價模型,并基于該模型創(chuàng)建了一個帶有模糊度等級標簽的模糊車牌圖像數(shù)據(jù)庫;其次,在所創(chuàng)建的模糊車牌圖像數(shù)據(jù)庫的基礎(chǔ)上,利用投影字典對學(xué)習(xí)模型以及邏輯回歸模型建立了一個模糊度客觀評價模型。實驗結(jié)果表明,所建立的客觀評價模型取得的評價結(jié)果與主觀評價模型取得效果具有較好的一致性。3、將稀疏表示分類模型以及盲去模糊模型緊密聯(lián)合起來,建立了一個模糊車牌字符識別模型。所建立的模型將圖像去模糊以及圖像識別耦合在一個能量方程中,使得去模糊以及識別相互影響、相互促進,最終同時實現(xiàn)了對模糊字符圖像的恢復(fù)和識別。實驗結(jié)果表明,所建立的模型具有良好的去模糊以及識別效果。
[Abstract]:As an important part of intelligent transportation system, license plate recognition plays an irreplaceable role in people's daily life. Because of the bad weather and the relative motion between the vehicle and the camera, some of the license plate images are usually blurred. Therefore, it is necessary to establish an application system that can recognize the fuzzy license plate image. Through the detailed analysis of the characteristics of the fuzzy license plate image, and the in-depth research on the license plate recognition, image ambiguity evaluation, fuzzy image recognition, etc. The main work and research results of this paper include the following three aspects: 1. The common license plate image location, character segmentation and preprocessing methods are summarized. Combined with the characteristics of fuzzy license plate image which needs to be recognized, the method of location, character segmentation and preprocessing for fuzzy license plate image is proposed. Then, based on the above methods, the fuzzy character image database .2is established to evaluate the ambiguity of the fuzzy license plate image. The purpose of the ambiguity evaluation is to distinguish the seriously blurred license plate image from the relatively fuzzy license plate image. So only the fuzzy license plate image recognition model is designed. Firstly, a subjective evaluation model of fuzzy degree is established by combining the recognition ability of the human eye to the five characters behind the fuzzy license plate. Based on the model, a fuzzy license plate image database with fuzzy grade label is created. Secondly, the fuzzy license plate image database is created based on the fuzzy license plate image database. An objective evaluation model of fuzzy degree is established for learning model and logical regression model by using projection dictionary. The experimental results show that, The evaluation results obtained by the established objective evaluation model are in good agreement with the results obtained by the subjective evaluation model. The sparse representation classification model and the blind de-fuzzy model are closely combined. In this paper, a fuzzy license plate character recognition model is established, which combines image de-blurring and image recognition into an energy equation, which makes de-blurring and recognition interact and promote each other. Finally, the restoration and recognition of fuzzy character images are realized at the same time. The experimental results show that the proposed model has a good effect of de-blurring and recognition.
【學(xué)位授予單位】：南京郵電大學(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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