發(fā)布時(shí)間：2018-06-18 07:49

  本文選題：交通標(biāo)志檢測(cè) + 交通標(biāo)志識(shí)別��； 參考：《長(zhǎng)安大學(xué)》2017年碩士論文

【摘要】：隨著科技的迅猛發(fā)展,汽車成為日常生活中人們出行必不可少的交通工具,由于機(jī)動(dòng)車數(shù)量的持續(xù)增長(zhǎng),一系列交通問(wèn)題隨之而來(lái),比如交通安全、交通擁堵、交通污染等。在這復(fù)雜的交通問(wèn)題背景下,高級(jí)駕駛輔助系統(tǒng)(ADAS)應(yīng)運(yùn)而生。交通標(biāo)志檢測(cè)與識(shí)別作為ADAS的一個(gè)基礎(chǔ)分支,也是提高交通安全和效率的重要手段。所以本文對(duì)交通標(biāo)志檢測(cè)與識(shí)別方法進(jìn)行了相關(guān)研究。論文首先分析了顯著性檢測(cè)原理,結(jié)合交通標(biāo)志的相關(guān)特征,提出一種基于顯著特征的交通標(biāo)志檢測(cè)方法,并對(duì)該方法進(jìn)行實(shí)驗(yàn)驗(yàn)證;其次,通過(guò)對(duì)卷積神經(jīng)網(wǎng)絡(luò)的分析和研究,提出一種基于改進(jìn)的AlexNet卷積神經(jīng)網(wǎng)絡(luò)的交通標(biāo)志識(shí)別方法。本文主要研究?jī)?nèi)容及成果包括以下幾個(gè)方面:(1)提出一種基于顯著性的交通標(biāo)志檢測(cè)方法。首先通過(guò)對(duì)交通標(biāo)志的顏色特征、邊界特征和位置信息這三個(gè)視覺(jué)特征進(jìn)行顯著性建模;其次建立顯著特征融合規(guī)則,并結(jié)合代價(jià)函數(shù)的最小化,融合輸入圖像的特征,得到最終最優(yōu)顯著圖;最后,對(duì)最優(yōu)顯著圖進(jìn)行二值化處理,提取并標(biāo)記二值圖中的連通區(qū)域,將其映射到原始RGB圖中,使用滑動(dòng)窗口法提取感興趣區(qū)域,實(shí)現(xiàn)交通標(biāo)志的檢測(cè)。實(shí)驗(yàn)結(jié)果證明本算法適用于復(fù)雜環(huán)境下的交通標(biāo)志的檢測(cè),并通過(guò)與4種常用的顯著檢測(cè)算法對(duì)比分析,證明本文提出的檢測(cè)算法相比較于其它算法具有較高的顯著性檢測(cè)性能。(2)提出一種基于改進(jìn)的AlexNet卷積神經(jīng)網(wǎng)絡(luò)的交通標(biāo)志識(shí)別方法。該方法首先分析了AlexNet網(wǎng)絡(luò)模型的結(jié)構(gòu),并在此網(wǎng)絡(luò)的基礎(chǔ)上對(duì)網(wǎng)絡(luò)的結(jié)構(gòu)和參數(shù)進(jìn)行調(diào)整優(yōu)化,得到新的AlexNet網(wǎng)絡(luò)模型;然后使用新的網(wǎng)絡(luò)模型對(duì)交通標(biāo)志進(jìn)行識(shí)別。利用改進(jìn)后的AlexNet卷積神經(jīng)網(wǎng)絡(luò)進(jìn)行交通標(biāo)志的識(shí)別主要包括兩部分內(nèi)容,一是AlexNet網(wǎng)絡(luò)模型的訓(xùn)練;二是利用訓(xùn)練好的AlexNet模型實(shí)現(xiàn)對(duì)輸入的分類。(3)提出一種訓(xùn)練數(shù)據(jù)集擴(kuò)充方法。本文選用德國(guó)交通標(biāo)志識(shí)別數(shù)據(jù)集(GTSRB)對(duì)提出的AlexNet模型進(jìn)行訓(xùn)練和測(cè)試,由于GTSRB訓(xùn)練樣本的不平衡,本文提出兩種樣本擴(kuò)充方法對(duì)數(shù)據(jù)集進(jìn)行改善。實(shí)驗(yàn)采用擴(kuò)充后的數(shù)據(jù)集和原始數(shù)據(jù)集對(duì)提出的Alex Net模型進(jìn)行訓(xùn)練和測(cè)試,結(jié)果表明,使用擴(kuò)充訓(xùn)練樣本集訓(xùn)練的AlexNet分類模型對(duì)交通標(biāo)志識(shí)別,測(cè)試集中大部分類別的交通標(biāo)志能夠達(dá)到95%以上的識(shí)別準(zhǔn)確率,高于原始訓(xùn)練集的93%,通過(guò)實(shí)驗(yàn),對(duì)比分析本文提出的Alex Net網(wǎng)絡(luò)、LeNet卷積神經(jīng)網(wǎng)絡(luò)和經(jīng)典的“Hog+SVM”分類器,證明本文提出的識(shí)別方法,無(wú)論是識(shí)別精準(zhǔn)率還是時(shí)間復(fù)雜度方面,均優(yōu)于另外兩種方法。
[Abstract]:With the rapid development of science and technology, automobile becomes an indispensable vehicle in daily life. Because of the continuous growth of the number of vehicles, a series of traffic problems, such as traffic safety, traffic congestion, traffic pollution and so on. In the context of this complex traffic problem, Advanced driving Assistance system (ADASS) emerged as the times require. As a basic branch of ADAS, traffic sign detection and recognition is also an important means to improve traffic safety and efficiency. Therefore, this paper carries on the correlation research to the traffic sign detection and the recognition method. In this paper, the principle of significance detection is analyzed, and a new method of traffic sign detection based on salient features is proposed, which is verified by experiments. Based on the analysis and research of convolution neural network, a traffic sign recognition method based on improved AlexNet convolution neural network is proposed. The main contents and achievements of this paper include the following aspects: 1) A signal-based traffic sign detection method is proposed. Firstly, the visual features of traffic signs, such as color features, boundary features and location information, are modeled significantly. Secondly, the fusion rules of salient features are established, and the features of input images are fused with the minimization of the cost function. The final optimal salience map is obtained. Finally, the connected region in the binary map is extracted and marked, and mapped to the original RGB map, and the region of interest is extracted by sliding window method. The detection of traffic signs is realized. The experimental results show that the proposed algorithm is suitable for the detection of traffic signs in complex environments. It is proved that the proposed detection algorithm has higher significant detection performance than other algorithms.) A traffic sign recognition method based on improved AlexNet convolution neural network is proposed. Firstly, the structure of AlexNet network model is analyzed, and the structure and parameters of the network are adjusted and optimized on the basis of this network, and a new AlexNet network model is obtained, and then the new network model is used to identify traffic signs. The traffic sign recognition based on the improved AlexNet convolution neural network includes two parts: one is the training of the AlexNet network model, the other is to use the trained AlexNet model to realize the classification of the input. In this paper, the German Traffic sign recognition dataset (GTSRB) is used to train and test the proposed AlexNet model. Due to the imbalance of GTSRB training samples, two methods of sample expansion are proposed to improve the data set. The extended data set and the original data set are used to train and test the proposed Alex net model. The results show that the AlexNet classification model trained by the extended training sample set is used to recognize traffic signs. Most types of traffic signs in the test set can achieve more than 95% recognition accuracy, which is higher than 93% of the original training set. Through experiments, the paper compares and analyzes the Alex net network LeNet convolution neural network and the classical "Hog SVM" classifier. It is proved that the method proposed in this paper is superior to the other two methods in terms of accuracy rate and time complexity.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP183

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 周廣波;李豪杰;;一種基于顏色不變量和PHOG特征的交通標(biāo)志檢測(cè)方法[J];計(jì)算機(jī)應(yīng)用與軟件;2014年08期

2 常發(fā)亮;黃翠;劉成云;趙永國(guó);馬傳峰;;基于高斯顏色模型和SVM的交通標(biāo)志檢測(cè)[J];儀器儀表學(xué)報(bào);2014年01期

3 劉建偉;黎海恩;羅雄麟;;概率圖模型學(xué)習(xí)技術(shù)研究進(jìn)展[J];自動(dòng)化學(xué)報(bào);2014年06期

4 王春瑤;陳俊周;李煒;;超像素分割算法研究綜述[J];計(jì)算機(jī)應(yīng)用研究;2014年01期

5 何耀平;徐麗珍;;自然場(chǎng)景下交通標(biāo)志的自動(dòng)識(shí)別算法[J];微計(jì)算機(jī)信息;2010年04期

6 黃志勇,孫光民,李芳;基于RGB視覺(jué)模型的交通標(biāo)志分割[J];微電子學(xué)與計(jì)算機(jī);2004年10期

相關(guān)碩士學(xué)位論文 前2條

1 王洋;一種基于模板匹配的交通標(biāo)志識(shí)別方法[D];吉林大學(xué);2013年

2 賴敏;基于AdaBoost迭代學(xué)習(xí)的支持向量機(jī)分類算法[D];重慶師范大學(xué);2010年

，

本文編號(hào)：2034736