【摘要】：車牌識別技術(shù)是智能交通系統(tǒng)中的重要組成部分,是計(jì)算機(jī)視覺、圖像處理與模式識別在智能交通領(lǐng)域的重要研究課題之一。但在實(shí)際環(huán)境下采集到的車牌圖像,容易受到光照變化、尺度變化、目標(biāo)干擾等諸多不利因素影響,因此在復(fù)雜多變的自然下識別車牌仍然是一個(gè)十分具有挑戰(zhàn)的課題。車牌識別技術(shù)主要解決車牌的定位、分割、識別三個(gè)問題。本文分別對這三個(gè)部分進(jìn)行了研究,并提出了相應(yīng)算法。本文提出了一種基于目標(biāo)區(qū)域的車牌定位算法,采用逐步求精的定位策略。該算法適用于光照變化、尺度變化和目標(biāo)干擾等復(fù)雜的自然環(huán)境。本文引入了Selective Search算法對輸入圖像進(jìn)行目標(biāo)區(qū)域提取,根據(jù)車牌特征篩選出車牌候選區(qū)域,并通過一個(gè)預(yù)訓(xùn)練的支持向量機(jī)對候選區(qū)域進(jìn)行判別,保留車牌區(qū)域。對獲得車牌區(qū)域進(jìn)行非極大值(NMS)抑制剔除重合區(qū)域。最后精確定位到車牌位置。本文提出了一種基于連通區(qū)域的字符分割算法。該算法首先對輸入車牌進(jìn)行預(yù)處理和傾斜校正,結(jié)合連通區(qū)域標(biāo)記法和數(shù)學(xué)形態(tài)學(xué)處理法獲得字符區(qū)域。同時(shí),本文對傳統(tǒng)的字符歸一化方法進(jìn)行了改進(jìn),有效的解決了由字符歸一化造成的字符形變的問題。本文提出了一種基于卷積神經(jīng)網(wǎng)絡(luò)的車牌字符識別算法,設(shè)計(jì)了兩個(gè)卷積網(wǎng)絡(luò)NET1和NET2,其中NET1用做識別漢字,NET2用做識別字母和數(shù)字。本文引入了 rectifier作為神經(jīng)元的激活函數(shù),并使用mini-batch隨機(jī)梯度下降法訓(xùn)練網(wǎng)絡(luò),可以加速目標(biāo)函數(shù)的收斂。采用卷積神經(jīng)網(wǎng)絡(luò)可以從輸入的字符圖像中自動(dòng)提取出圖像特征,并進(jìn)行分類,從而獲得識別結(jié)果。在整個(gè)過程中,不需要手動(dòng)選定圖像特征或?qū)D像作局部處理。實(shí)驗(yàn)表明,本文算法可以有效的在復(fù)雜自然環(huán)境中定位車牌,分割字符和識別字符。并將本文算法與同類型算法做了比較,均有顯著的提升。
[Abstract]:License plate recognition technology is an important part of intelligent transportation system. It is one of the important research topics of computer vision, image processing and pattern recognition in the field of intelligent transportation. However, the license plate images collected in the actual environment are easily affected by many unfavorable factors, such as light change, scale change, target interference and so on, so it is still a challenge to recognize the license plate in the complex and changeable nature. License plate recognition technology mainly solves three problems of license plate location, segmentation and recognition. In this paper, the three parts are studied, and the corresponding algorithms are proposed. A license plate location algorithm based on target region is proposed in this paper. The algorithm is suitable for complex natural environment, such as illumination variation, scale change and target interference. In this paper, the Selective Search algorithm is introduced to extract the target region of the input image, and the candidate region is selected according to the license plate feature, and the candidate region is identified by a pre-trained support vector machine to preserve the license plate area. Non-maximum (NMS) suppression is used to eliminate the coincidence area of the obtained license plate. Finally, the location of the license plate is accurately located. In this paper, a character segmentation algorithm based on connected region is proposed. The algorithm firstly preprocesses and corrects the input license plate, and combines the connected region marking method and the mathematical morphology processing method to obtain the character region. At the same time, the traditional method of character normalization is improved, which effectively solves the problem of character deformation caused by character normalization. A license plate character recognition algorithm based on convolution neural network is presented in this paper. Two convolution networks NET1 and NET2, are designed in which NET1 is used to recognize Chinese characters and NET2 is used to recognize letters and numbers. In this paper, rectifier is introduced as the activation function of neurons, and the mini-batch stochastic gradient descent method is used to train the network, which can accelerate the convergence of the objective function. By using convolution neural network, the image features can be automatically extracted from the input character images and classified, and the recognition results can be obtained. In the whole process, there is no need to manually select image features or make partial image processing. Experimental results show that the proposed algorithm can effectively locate license plates, segment characters and recognize characters in complex environments. This algorithm is compared with the same type algorithm, and has a significant improvement.
【學(xué)位授予單位】：山東大學(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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