【摘要】：車輛識別技術(shù)是智能交通系統(tǒng)技術(shù)的重要關(guān)鍵技術(shù)之一,在軍事車輛管理中有重要應(yīng)用價(jià)值。車標(biāo)定位識別技術(shù)是車輛識別技術(shù)的重要研究領(lǐng)域。車標(biāo)定位技術(shù)往往需要車牌定位作為重要的輔助,快速、準(zhǔn)確、高魯棒性的車牌定位、車標(biāo)定位是智能交通系統(tǒng)的研究熱點(diǎn)。本文提出兩個(gè)具有顏色不變性的顏色比例用于車牌顏色提取,實(shí)現(xiàn)車牌快速、準(zhǔn)確、魯棒性定位,完成車標(biāo)粗定位;提出車標(biāo)定位改進(jìn)算法,提高定位精度。在基于車牌定位的車標(biāo)粗定位中,基于RGB顏色模型本文提出了兩個(gè)顏色比例,基于雙色反射模型從理論上證明了它們具有顏色不變性,并首次采用這兩個(gè)顏色比例提取車牌顏色特征,用于基于全局顏色特征和局部邊緣特征相結(jié)合的車牌定位算法中。對1078幅交通攝像頭實(shí)拍車輛圖像進(jìn)行處理,車牌定位成功率為99.9%。車輛原圖大小約1000×1000像素時(shí),車牌平均定位時(shí)間約0.2秒。與常用的基于HSV顏色模型的車牌定位方法相比,后者定位成功率是92.12%,平均定位時(shí)間約0.35秒。本文方法明顯好于基于HSV模型的車牌定位方法。經(jīng)比較發(fā)現(xiàn)本文顏色比例提取藍(lán)色和HSV模型提取藍(lán)色可以實(shí)現(xiàn)優(yōu)勢互補(bǔ)。利用車牌定位結(jié)果進(jìn)而實(shí)現(xiàn)車標(biāo)粗定位�？焖佟⒏咝У能嚇�(biāo)識別需要準(zhǔn)確的車標(biāo)定位。為進(jìn)一步抑制柵格背景干擾,提高車標(biāo)定位精度,本文提出基于邊緣梯度角度直方圖分析和局部多閾值處理相結(jié)合的車標(biāo)定位改進(jìn)算法。分析邊緣梯度角度直方圖,將車標(biāo)同柵格背景進(jìn)一步分離。采用局部多閾值處理的方法消除光照不均的影響,得到更完整的車標(biāo)。對5個(gè)品牌車輛67幅車標(biāo)圖像進(jìn)行處理,所有車標(biāo)均能較完整、準(zhǔn)確地定位,車標(biāo)定位率為100%。與常用的基于模板匹配的車標(biāo)定位方法相比,后者雖能定位出所有車標(biāo),但誤差較大的有28幅,占41.79%。本文方法定位精度明顯好于模板法的有53幅,占79.1%。對模板法誤差圖像逐一分析發(fā)現(xiàn)模板法對柵格背景要求更高。以上結(jié)果說明本文方法定位效果優(yōu)于基于模板匹配的車標(biāo)定位方法,魯棒性更強(qiáng)。車標(biāo)粗定位區(qū)域約為260×170像素時(shí),本文方法平均定位時(shí)間為0.1秒,模板法平均定位時(shí)間為0.04秒。模板法比本文方法要簡單,速度更快。不過本文方法基本能滿足實(shí)時(shí)性要求。
[Abstract]:Vehicle recognition technology is one of the important key technologies of intelligent transportation system technology, and it has important application value in military vehicle management. Vehicle mark location and recognition technology is an important research field of vehicle recognition technology. Vehicle mark location technology often needs license plate location as an important auxiliary, fast, accurate and highly robust license plate location. Vehicle mark location is the research focus of intelligent transportation system. In this paper, two color ratios with color invariance are proposed for license plate color extraction to realize fast, accurate and robust location of license plate, and to complete the rough location of vehicle mark, and an improved algorithm of vehicle mark location is proposed to improve the positioning accuracy. In the rough location of vehicle marks based on license plate location, based on RGB color model, two color ratios are proposed in this paper. Based on the two-color reflection model, it is proved theoretically that they have color invariance. For the first time, these two color ratios are used to extract license plate color features, which are used in license plate location algorithm based on the combination of global color features and local edge features. 1078 vehicle images taken by traffic camera are processed, and the success rate of license plate location is 99.9%. When the original size of the vehicle is about 1000 脳 1000 pixels, the average positioning time of the license plate is about 0.2 seconds. Compared with the common license plate location method based on HSV color model, the success rate of the latter is 92.12%, and the average positioning time is about 0.35 seconds. This method is obviously better than the license plate location method based on HSV model. It is found that blue extracted by color proportion and blue extracted by HSV model can complement each other. The rough location of vehicle mark is realized by using the result of license plate location. Rapid and efficient identification of vehicle signs requires accurate positioning of vehicle signs. In order to further suppress the grid background interference and improve the positioning accuracy of vehicle marks, an improved algorithm based on edge gradient angle histogram analysis and local multi-threshold processing is proposed in this paper. The edge gradient angle histogram is analyzed, and the vehicle mark is further separated from the grid background. The local multi-threshold processing method is used to eliminate the influence of uneven light, and a more complete vehicle mark is obtained. The images of 67 signs of 5 brand vehicles are processed, and all the signs can be located completely and accurately, and the positioning rate of the signs is 100%. Compared with the commonly used vehicle mark location method based on template matching, the latter can locate all the vehicle marks, but 28 of them have large errors, accounting for 41.79%. The positioning accuracy of this method is obviously better than that of template method, accounting for 79.1%. It is found that the template method requires higher grid background by analyzing the error images of template method one by one. The above results show that the localization effect of this method is better than that of the vehicle mark location method based on template matching, and the robustness of the proposed method is stronger than that of the template matching method. When the rough positioning area of the vehicle mark is about 260 脳 170 pixels, the average positioning time of this method is 0.1 seconds, and the average positioning time of the template method is 0.04 seconds. Template method is simpler and faster than this method. However, this method can basically meet the real-time requirements.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U495;TP391.41

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本文編號：2484691