本文關(guān)鍵詞： 交通標志檢測與識別 最大穩(wěn)定極值區(qū)域 感興趣區(qū)域提取 多特征融合 SVM分類　出處：《山東大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：據(jù)相關(guān)機構(gòu)統(tǒng)計全世界每年有130萬人左右因為道路交通事故而喪失珍貴的生命,其中與駕駛員自身因素相關(guān)的酒后或疲勞駕駛、超速行駛等成為了這些交通安全事故的主要誘因。交通事故不僅會造成巨大的經(jīng)濟損失,更重要的是會無情地奪取人類寶貴的生命,因此道路交通安全問題已不再是某個國家面臨的問題,而是需要全世界各國共同解決的。為了有效提高道路交通安全和運輸效率,降低事故發(fā)生頻率,保障人們的人身財產(chǎn)安全,智能交通系統(tǒng)應(yīng)運而生。交通標志識別系統(tǒng)是智能交通系統(tǒng)諸多先進技術(shù)領(lǐng)域中的一個重要分支,在無人駕駛車輛、智能機器人、輔助駕駛系統(tǒng)、輔助道路標志規(guī)劃、導(dǎo)盲機器人等方面都具有廣闊的發(fā)展和應(yīng)用前景。因此對于交通標志識別系統(tǒng)相關(guān)技術(shù)的研究和探索非常具有學(xué)術(shù)意義和實用價值。本文以城市道路中常見的指示、禁令以及警告標志為研究對象,針對大場景自然環(huán)境下的道路交通標志的檢測與識別問題展開研究和討論,主要從高分辨率大場景下的快速交通標志檢測、多類別交通標志的魯棒識別和交通標志識別系統(tǒng)平臺的設(shè)計與搭建這三個方面作了深入研究和探索。在交通標志檢測方面,為解決傳統(tǒng)的基于機器學(xué)習的交通標志檢測方法需要對每一個待檢測子窗口進行處理而導(dǎo)致算法實時性欠佳的問題,提出了顏色增強下的MSER提取標志候選區(qū)域結(jié)合線性SVM的快速交通標志檢測方法。該方法根據(jù)標志的顏色進行顏色增強,對增強圖像提取MSER得到交通標志感興趣區(qū)域,然后在大場景高分辨率圖像的多尺度滑動遍歷檢測搜索過程中僅對包含交通標志候選區(qū)域的滑動窗口進行HOG特征的提取和SVM分類判別,而對非標志候選區(qū)域的滑動窗口則不進行特征提取和分類判別。實驗結(jié)果表明:改進的MSER+HOG+SVM方法在獲得了較高的檢測準確率以及較低的誤檢率的前提下,運算速度上有較大提升,且魯棒性較好。在多類別交通標志識別方面,提出了融合全局特征和局部特征的多特征交通標志分類識別方法,有效地提升了識別度。該方法首先分別提取能夠描述標志圖像內(nèi)部紋理信息的LBP特征、表示標志圖像形狀信息的HOG特征以及描述圖像粗略輪廓信息的全局Gist特征,然后采用線性組合方式,實現(xiàn)特征融合互補,并通過主成分分析方法進行數(shù)據(jù)降維,最后采用支持向量機分類器進行交通標志訓(xùn)練與識別。實驗結(jié)果表明:相對于提取單一特征的交通標志識別方法,基于多特征融合的算法獲得了更高的識別精確度,同時也滿足實時性要求。最后,本文以輪式機器人為主要硬件基礎(chǔ),利用Microsoft Visual Studio 2010結(jié)合OpenCV開源視覺庫設(shè)計了基于MFC對話框的交通標志識別系統(tǒng)應(yīng)用程序以模擬行車駕駛環(huán)境。系統(tǒng)平臺主要集成了圖像采集與實時處理、標志檢測、標志識別和機器人運動控制等功能模塊。
[Abstract]:According to the statistics of relevant organizations, there are about 1.3 million people in the world who lose their precious lives because of road traffic accidents every year, including drunk or fatigue driving related to drivers' own factors. Speeding has become the main cause of these traffic safety accidents. Traffic accidents will not only cause huge economic losses, more importantly, will ruthlessly take away the precious lives of human beings. Therefore, the problem of road traffic safety is no longer a problem faced by a certain country, but needs to be solved by all countries all over the world. In order to effectively improve road traffic safety and transport efficiency, reduce the frequency of accidents. The intelligent transportation system (its) emerges as the times require. Traffic sign recognition system is an important branch in many advanced fields of intelligent transportation system, which is used in driverless vehicles and intelligent robots. Auxiliary driving system, auxiliary road sign planning. Blind robot has a broad prospect of development and application. Therefore, the research and exploration of traffic sign recognition system is of great academic significance and practical value. Show. Ban and warning signs as the research object, the detection and recognition of road traffic signs under the large scene environment is studied and discussed, mainly from the high resolution of the rapid traffic signs detection. The design and construction of robust recognition and traffic sign recognition system platform for multi-class traffic signs are studied and explored in detail. In order to solve the problem that the traditional traffic sign detection method based on machine learning needs to deal with every sub-window to be detected, which leads to poor real-time algorithm. A fast traffic sign detection method based on MSER and linear SVM is proposed, which is based on the color of the sign. The area of interest is obtained by extracting MSER from enhanced image. Then in the search process of multi-scale sliding traversal detection of large scene high-resolution images, only the HOG feature extraction and SVM classification are carried out on the sliding window containing traffic sign candidate area. But the sliding window of unmarked candidate region is not extracted and classified. The experimental results show that the improved MSER HOG is improved. The SVM method can obtain higher detection accuracy and lower false detection rate. In the aspect of multi-class traffic sign recognition, a multi-feature traffic sign classification and recognition method combining global features and local features is proposed. The recognition degree is improved effectively. Firstly, the LBP features which can describe the internal texture information of the logo image are extracted separately. The HOG features representing the shape information of the logo image and the global Gist features describing the rough contour information of the image are presented. Then the features are fused and complemented by linear combination. Finally, the support vector machine classifier is used to train and recognize traffic signs. The experimental results show that the traffic sign recognition method is relative to extracting a single feature. The algorithm based on multi-feature fusion achieves higher recognition accuracy and meets the real-time requirements. Finally, this paper takes wheeled robot as the main hardware base. Using Microsoft Visual Studio. In order to simulate the driving environment, the application program of traffic sign recognition system based on MFC dialog box is designed based on OpenCV open source visual library in 2010. The system platform mainly integrates image acquisition and real-time processing. Sign detection, sign recognition and robot motion control and other functional modules.
【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】：U463.6;U495;TP391.41

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