發(fā)布時(shí)間：2018-11-25 12:57

【摘要】：近年來(lái),我國(guó)汽車的保有量迅猛增長(zhǎng),交通運(yùn)輸面臨著越來(lái)越大的壓力和挑戰(zhàn),智能交通是現(xiàn)代交通運(yùn)輸業(yè)的發(fā)展方向。車輛識(shí)別是智能交通中的關(guān)鍵技術(shù),具有重要的商業(yè)價(jià)值和現(xiàn)實(shí)意義。將車牌識(shí)別技術(shù)與車標(biāo)識(shí)別技術(shù)結(jié)合,能夠獲取更準(zhǔn)確的車輛信息,提高車輛識(shí)別系統(tǒng)的識(shí)別率和魯棒性。本文的車標(biāo)識(shí)別系統(tǒng)分為車標(biāo)定位和車標(biāo)分類兩個(gè)階段。在車標(biāo)定位階段,使用基于車牌先驗(yàn)知識(shí)的由粗到精的定位方法,先根據(jù)車牌在RGB彩色空間和HSV彩色空間的顏色特征,定位車牌位置。然后根據(jù)車牌與車標(biāo)的相對(duì)拓?fù)潢P(guān)系粗略定位車標(biāo)。最后檢測(cè)車標(biāo)粗定位區(qū)域圖像邊緣,將水平邊緣圖像和垂直邊緣圖像相與,消除車標(biāo)周圍柵格的干擾,通過(guò)數(shù)學(xué)形態(tài)學(xué)開運(yùn)算濾掉了邊緣圖中的噪聲。最后使用邊緣投影和圖像重心的方法,以重心為起始位置,在水平和垂直投影上分別定位車標(biāo)上下左右邊界。在車標(biāo)分類的階段,首先把車標(biāo)圖像歸一化為32×32,從中提取出144維的HOG特征,輸入SVM線性分類器判斷車標(biāo)類別。利用以上車標(biāo)識(shí)別方法,本文使用Open CV4Android機(jī)器視覺庫(kù),首次在Android智能手機(jī)平臺(tái)設(shè)計(jì)開發(fā)了車標(biāo)識(shí)別移動(dòng)應(yīng)用程序。其中,使用OpenCV Manager管理OpenCV動(dòng)態(tài)鏈接庫(kù),通過(guò)CvCameraViewListener接口獲取到了攝像頭圖像。結(jié)果表明,本文采用的車標(biāo)識(shí)別算法快速、有效,車標(biāo)識(shí)別移動(dòng)應(yīng)用程序的平均處理時(shí)間小于0.2秒,滿足實(shí)時(shí)性的要求。
[Abstract]:In recent years, with the rapid growth of automobile ownership in China, traffic and transportation are facing more and more pressure and challenge. Intelligent transportation is the development direction of modern transportation industry. Vehicle identification is a key technology in intelligent transportation, which has important commercial value and practical significance. The combination of license plate recognition technology and vehicle sign recognition technology can obtain more accurate vehicle information and improve the recognition rate and robustness of vehicle recognition system. The identification system is divided into two stages: vehicle sign location and vehicle mark classification. In the phase of vehicle mark location, the location of vehicle license plate is located according to the color features of the license plate in RGB color space and HSV color space based on the priori knowledge of license plate. Then according to the relative topological relationship between the license plate and the vehicle sign, the vehicle logo is roughly located. Finally, the edge of the rough location area image is detected, the horizontal edge image and the vertical edge image are combined with each other, the interference of grid around the vehicle mark is eliminated, and the noise in the edge image is filtered by mathematical morphology. Finally, the edge projection and the image barycenter are used to locate the upper and lower sides of the vehicle mark on the horizontal and vertical projection, taking the center of gravity as the starting position. In the stage of vehicle mark classification, the vehicle mark image is normalized to 32 脳 32, and the 144-dimensional HOG feature is extracted from it, and the SVM linear classifier is input to judge the vehicle mark category. Using the above identification method, this paper designs and develops the mobile application program of vehicle logo recognition on Android smart phone platform using Open CV4Android machine vision library for the first time. Among them, OpenCV Manager is used to manage the OpenCV dynamic link library, and the camera image is obtained through the CvCameraViewListener interface. The results show that the algorithm used in this paper is fast and effective, and the average processing time of the mobile application is less than 0.2 seconds, which meets the requirement of real-time.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495

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本文編號(hào)：2356187