發(fā)布時間：2018-06-18 07:49

  本文選題：交通標志檢測 + 交通標志識別　； 參考：《長安大學》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,由于機動車數(shù)量的持續(xù)增長,一系列交通問題隨之而來,比如交通安全、交通擁堵、交通污染等。在這復雜的交通問題背景下,高級駕駛輔助系統(tǒng)(ADAS)應運而生。交通標志檢測與識別作為ADAS的一個基礎(chǔ)分支,也是提高交通安全和效率的重要手段。所以本文對交通標志檢測與識別方法進行了相關(guān)研究。論文首先分析了顯著性檢測原理,結(jié)合交通標志的相關(guān)特征,提出一種基于顯著特征的交通標志檢測方法,并對該方法進行實驗驗證;其次,通過對卷積神經(jīng)網(wǎng)絡的分析和研究,提出一種基于改進的AlexNet卷積神經(jīng)網(wǎng)絡的交通標志識別方法。本文主要研究內(nèi)容及成果包括以下幾個方面:(1)提出一種基于顯著性的交通標志檢測方法。首先通過對交通標志的顏色特征、邊界特征和位置信息這三個視覺特征進行顯著性建模;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,對最優(yōu)顯著圖進行二值化處理,提取并標記二值圖中的連通區(qū)域,將其映射到原始RGB圖中,使用滑動窗口法提取感興趣區(qū)域,實現(xiàn)交通標志的檢測。實驗結(jié)果證明本算法適用于復雜環(huán)境下的交通標志的檢測,并通過與4種常用的顯著檢測算法對比分析,證明本文提出的檢測算法相比較于其它算法具有較高的顯著性檢測性能。(2)提出一種基于改進的AlexNet卷積神經(jīng)網(wǎng)絡的交通標志識別方法。該方法首先分析了AlexNet網(wǎng)絡模型的結(jié)構(gòu),并在此網(wǎng)絡的基礎(chǔ)上對網(wǎng)絡的結(jié)構(gòu)和參數(shù)進行調(diào)整優(yōu)化,得到新的AlexNet網(wǎng)絡模型;然后使用新的網(wǎng)絡模型對交通標志進行識別。利用改進后的AlexNet卷積神經(jīng)網(wǎng)絡進行交通標志的識別主要包括兩部分內(nèi)容,一是AlexNet網(wǎng)絡模型的訓練;二是利用訓練好的AlexNet模型實現(xiàn)對輸入的分類。(3)提出一種訓練數(shù)據(jù)集擴充方法。本文選用德國交通標志識別數(shù)據(jù)集(GTSRB)對提出的AlexNet模型進行訓練和測試,由于GTSRB訓練樣本的不平衡,本文提出兩種樣本擴充方法對數(shù)據(jù)集進行改善。實驗采用擴充后的數(shù)據(jù)集和原始數(shù)據(jù)集對提出的Alex Net模型進行訓練和測試,結(jié)果表明,使用擴充訓練樣本集訓練的AlexNet分類模型對交通標志識別,測試集中大部分類別的交通標志能夠達到95%以上的識別準確率,高于原始訓練集的93%,通過實驗,對比分析本文提出的Alex Net網(wǎng)絡、LeNet卷積神經(jīng)網(wǎng)絡和經(jīng)典的“Hog+SVM”分類器,證明本文提出的識別方法,無論是識別精準率還是時間復雜度方面,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.
【學位授予單位】：長安大學
【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：2034736