本文選題：卷積神經(jīng)網(wǎng)絡(luò)　切入點(diǎn)：智能交通系統(tǒng)　出處：《電子科技大學(xué)》2017年碩士論文

【摘要】：卷積神經(jīng)網(wǎng)絡(luò)是可以模擬人的大腦功能并能夠應(yīng)用到更多領(lǐng)域的一種特殊神經(jīng)網(wǎng)絡(luò),它是近年發(fā)展起來(lái),相比于其他同類(lèi)方法,卷積神經(jīng)網(wǎng)絡(luò)具有理論完備、泛化性能好、全局性能優(yōu)化、適應(yīng)性強(qiáng)等優(yōu)點(diǎn)。卷積神經(jīng)網(wǎng)絡(luò)是目前機(jī)器學(xué)習(xí)領(lǐng)域的研究熱點(diǎn)。作為一種新興技術(shù),卷積神經(jīng)網(wǎng)絡(luò)在很多應(yīng)用領(lǐng)域的研究還不成熟,有待進(jìn)一步的探索和完善。現(xiàn)如今,利用高新技術(shù),智能交通系統(tǒng)對(duì)傳統(tǒng)的交通系統(tǒng)進(jìn)行的改造,發(fā)揮著巨大的效能也獲得了深厚的社會(huì)經(jīng)濟(jì)效益。隨著計(jì)算機(jī)網(wǎng)絡(luò)技術(shù)和通信技術(shù)的逐步發(fā)展,車(chē)輛牌照識(shí)別系統(tǒng)在越來(lái)越多的國(guó)家扮演著舉足輕重的角色。在現(xiàn)實(shí)生活中傳統(tǒng)的車(chē)牌識(shí)別系統(tǒng),早期階段的預(yù)處理可能導(dǎo)致車(chē)牌字符分割和定位不清的錯(cuò)誤和缺點(diǎn),這將影響到車(chē)牌識(shí)別的效果,減少實(shí)際的識(shí)別率。并且,傳統(tǒng)車(chē)牌識(shí)別方法的圖像預(yù)處理過(guò)程耗時(shí),無(wú)法應(yīng)對(duì)實(shí)際應(yīng)用中的實(shí)時(shí)性要求,并且容易受到噪聲影響,難以充分保留原始信號(hào),會(huì)進(jìn)一步降低識(shí)別效果。本文在研究分析卷積神經(jīng)網(wǎng)絡(luò)工作機(jī)理的基礎(chǔ)之上,將局部權(quán)值共享的卷積神經(jīng)網(wǎng)絡(luò)方法引入智能交通系統(tǒng)這一具體的應(yīng)用領(lǐng)域,通過(guò)多維網(wǎng)絡(luò)輸入向量圖像可以直接輸入這一特性,能夠在圖像識(shí)別和處理方面有較好的效果,避免了在特征提取的過(guò)程中的復(fù)雜度。本文針對(duì)基于卷積神經(jīng)網(wǎng)絡(luò)下的車(chē)輛牌照識(shí)別研究課題,整理歸納了國(guó)內(nèi)外學(xué)術(shù)界的研究現(xiàn)狀和成果,介紹了利用卷積神經(jīng)網(wǎng)絡(luò)進(jìn)行圖像識(shí)別的原理。在對(duì)經(jīng)典神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)LeNet-5的分析研究基礎(chǔ)上加以完善,將完善后的卷積神經(jīng)網(wǎng)絡(luò)ILeNeT-5應(yīng)用于車(chē)牌識(shí)別問(wèn)題中,并基于MATLAB平臺(tái),完成應(yīng)用程序的開(kāi)發(fā),最終完成基于卷積神經(jīng)網(wǎng)絡(luò)下的車(chē)輛牌照識(shí)別的研究工作。本文所研究的基于卷積神經(jīng)網(wǎng)絡(luò)下的車(chē)牌識(shí)別,是在神經(jīng)網(wǎng)絡(luò)的優(yōu)勢(shì)下,使用一種改進(jìn)的ILeNeT-5神經(jīng)網(wǎng)絡(luò)對(duì)車(chē)牌的識(shí)別,它優(yōu)化了網(wǎng)絡(luò)中卷積層和采樣層的參數(shù),在特殊情境下也提高了車(chē)牌的識(shí)別率,能有效的提高車(chē)牌識(shí)別度,對(duì)于智能交通系統(tǒng)的建設(shè)具有重大的社會(huì)意義。
[Abstract]:Convolutional neural network is a special neural network which can simulate human brain function and can be applied to more fields. It has been developed in recent years. Compared with other similar methods, convolutional neural network has perfect theory and good generalization performance. Global performance optimization, strong adaptability and so on. Convolution neural network is the research hotspot in the field of machine learning. As a new technology, the research of convolution neural network in many application fields is not mature. Need to be further explored and improved. Nowadays, the transformation of traditional transportation systems by using high and new technologies and intelligent transportation systems, With the development of computer network technology and communication technology, Vehicle license plate recognition system plays an important role in more and more countries. In the traditional license plate recognition system in real life, the preprocessing of early stage may lead to the errors and shortcomings of license plate character segmentation and unclear location. This will affect the effect of license plate recognition and reduce the actual recognition rate. Moreover, the image preprocessing process of the traditional license plate recognition method is time-consuming, unable to meet the real-time requirements of practical applications, and vulnerable to the impact of noise. It is difficult to fully retain the original signal, which will further reduce the recognition effect. In this paper, based on the analysis of the working mechanism of convolution neural network, The convolution neural network method of local weight sharing is introduced into the specific application field of intelligent transportation system. The multi-dimensional network input vector image can directly input this characteristic, and it has good effect in image recognition and processing. The complexity of feature extraction is avoided. In this paper, the current research situation and achievements of the domestic and foreign academic circles are summarized for the vehicle license plate recognition based on convolutional neural network. This paper introduces the principle of image recognition using convolutional neural network. Based on the analysis and research of classical neural network structure LeNet-5, the improved convolutional neural network ILeNeT-5 is applied to license plate recognition, and based on MATLAB platform. The research work of vehicle license plate recognition based on convolution neural network is finished, and the license plate recognition based on convolution neural network is studied in this paper, which is based on the advantage of neural network. An improved ILeNeT-5 neural network is used to recognize license plate. It optimizes the parameters of convolution layer and sampling layer in the network, and improves the recognition rate of license plate under special circumstances, which can effectively improve the recognition degree of license plate. It is of great social significance to the construction of intelligent transportation system.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41;TP183

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