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

【摘要】：卷積神經(jīng)網(wǎng)絡是可以模擬人的大腦功能并能夠應用到更多領域的一種特殊神經(jīng)網(wǎng)絡,它是近年發(fā)展起來,相比于其他同類方法,卷積神經(jīng)網(wǎng)絡具有理論完備、泛化性能好、全局性能優(yōu)化、適應性強等優(yōu)點。卷積神經(jīng)網(wǎng)絡是目前機器學習領域的研究熱點。作為一種新興技術,卷積神經(jīng)網(wǎng)絡在很多應用領域的研究還不成熟,有待進一步的探索和完善�，F(xiàn)如今,利用高新技術,智能交通系統(tǒng)對傳統(tǒng)的交通系統(tǒng)進行的改造,發(fā)揮著巨大的效能也獲得了深厚的社會經(jīng)濟效益。隨著計算機網(wǎng)絡技術和通信技術的逐步發(fā)展,車輛牌照識別系統(tǒng)在越來越多的國家扮演著舉足輕重的角色。在現(xiàn)實生活中傳統(tǒng)的車牌識別系統(tǒng),早期階段的預處理可能導致車牌字符分割和定位不清的錯誤和缺點,這將影響到車牌識別的效果,減少實際的識別率。并且,傳統(tǒng)車牌識別方法的圖像預處理過程耗時,無法應對實際應用中的實時性要求,并且容易受到噪聲影響,難以充分保留原始信號,會進一步降低識別效果。本文在研究分析卷積神經(jīng)網(wǎng)絡工作機理的基礎之上,將局部權值共享的卷積神經(jīng)網(wǎng)絡方法引入智能交通系統(tǒng)這一具體的應用領域,通過多維網(wǎng)絡輸入向量圖像可以直接輸入這一特性,能夠在圖像識別和處理方面有較好的效果,避免了在特征提取的過程中的復雜度。本文針對基于卷積神經(jīng)網(wǎng)絡下的車輛牌照識別研究課題,整理歸納了國內外學術界的研究現(xiàn)狀和成果,介紹了利用卷積神經(jīng)網(wǎng)絡進行圖像識別的原理。在對經(jīng)典神經(jīng)網(wǎng)絡結構LeNet-5的分析研究基礎上加以完善,將完善后的卷積神經(jīng)網(wǎng)絡ILeNeT-5應用于車牌識別問題中,并基于MATLAB平臺,完成應用程序的開發(fā),最終完成基于卷積神經(jīng)網(wǎng)絡下的車輛牌照識別的研究工作。本文所研究的基于卷積神經(jīng)網(wǎng)絡下的車牌識別,是在神經(jīng)網(wǎng)絡的優(yōu)勢下,使用一種改進的ILeNeT-5神經(jīng)網(wǎng)絡對車牌的識別,它優(yōu)化了網(wǎng)絡中卷積層和采樣層的參數(shù),在特殊情境下也提高了車牌的識別率,能有效的提高車牌識別度,對于智能交通系統(tǒng)的建設具有重大的社會意義。
[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.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP183

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本文編號：1691466