本文選題：智能車輛 + 自適應(yīng)閾值分割�。� 參考：《哈爾濱工業(yè)大學(xué)》2015年碩士論文

【摘要】：隨著我國(guó)高速公路建設(shè)的飛速發(fā)展和人均汽車占有量的日益增加,交通安全方面的問(wèn)題已經(jīng)成為不可忽視的問(wèn)題。因此,能夠?qū)η胺杰囕v進(jìn)行實(shí)時(shí)檢測(cè)和測(cè)距是車輛安全行駛和自主導(dǎo)航的重要措施,也是智能交通系統(tǒng)研究領(lǐng)域的熱點(diǎn)之一。雖然目前對(duì)車輛的檢測(cè)有很多經(jīng)典的檢測(cè)算法,如,幀差法,光流法等,但是這些檢測(cè)算法不太適合檢測(cè)前方運(yùn)動(dòng)中的車輛。論文根據(jù)前方車底陰影的特性提出了一種有效的檢測(cè)算法,并且通過(guò)比較基于不同傳感器的測(cè)距方法的優(yōu)缺點(diǎn)選擇了基于機(jī)器視覺(jué)(單雙目)的測(cè)距方法,實(shí)現(xiàn)對(duì)前方最近車輛距離的測(cè)量,為駕駛員提供準(zhǔn)確的輔助駕駛信息。論文主要完成了以下工作:(1)介紹了各種不同方式的車輛檢測(cè)算法,分析各種算法的適用場(chǎng)景及優(yōu)缺點(diǎn)。闡述了基于紅外線、超聲波等傳感器前方車輛測(cè)距的弊端,突出了機(jī)器視覺(jué)測(cè)距技術(shù)的應(yīng)用前景。(2)根據(jù)車輛底部始終穩(wěn)定的存在陰影這一特性,采用兩次自適應(yīng)閾值分割算法把車輛從復(fù)雜的實(shí)際背景下分割出來(lái),并生成車輛假設(shè)區(qū)域。根據(jù)車輛尾部的對(duì)稱性,采用sobel算子的邊緣檢測(cè)計(jì)算其對(duì)稱性測(cè)度,以便過(guò)濾掉已生成的虛假車輛,保留真實(shí)車輛。(3)研究攝像機(jī)成像原理,利用張正友平面標(biāo)定法對(duì)攝像機(jī)內(nèi)部參數(shù)進(jìn)行標(biāo)定,然后分別采用單目視覺(jué)測(cè)距法和雙目視差測(cè)距法對(duì)前方車輛實(shí)時(shí)測(cè)距,并且分析比較兩種方式的測(cè)量結(jié)果。(4)在Lab VIEW平臺(tái)上開發(fā)基于機(jī)器視覺(jué)的前方車輛檢測(cè)與測(cè)距系統(tǒng)軟件,利用Lab VIEW的計(jì)算機(jī)視覺(jué)庫(kù)完成系統(tǒng)各個(gè)模塊的開發(fā),并在實(shí)際的道路環(huán)境中進(jìn)行系統(tǒng)實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明,本系統(tǒng)能夠較準(zhǔn)確的檢測(cè)到前方車輛,并且兩車之間的距離最近可以測(cè)到10米以內(nèi),最遠(yuǎn)可以測(cè)到50米左右,滿足安全車距50米的要求。
[Abstract]:With the rapid development of highway construction and the increase of per capita vehicle possession, traffic safety has become a problem that can not be ignored. Therefore, it is an important measure for vehicle safety and autonomous navigation to be able to detect and range the vehicle in real time. It is also one of the hot spots in the research field of Intelligent Transportation system (its). Although there are many classical detection algorithms for vehicle detection, such as frame difference method, optical flow method and so on, these detection algorithms are not suitable for detecting vehicles in the front motion. In this paper, an effective detection algorithm is proposed according to the characteristics of the shadow of the front car bottom, and by comparing the advantages and disadvantages of the ranging methods based on different sensors, the ranging method based on machine vision (mono-binocular) is selected. To realize the distance measurement of the nearest vehicle in front and provide the accurate auxiliary driving information for the driver. The main work of this paper is as follows: 1) introduce various vehicle detection algorithms, analyze the applicable scenarios, advantages and disadvantages of the algorithms. Based on infrared, ultrasonic and other sensors, the disadvantages of vehicle ranging in front of vehicle are expounded, and the application prospect of ranging technology of machine vision is highlighted. (2) according to the characteristic that the shadow always exists in the bottom of the vehicle, The two-time adaptive threshold segmentation algorithm is used to segment the vehicle from the complex background and generate the vehicle hypothesis region. According to the symmetry of the vehicle tail, the symmetry measure is calculated by using the edge detection of the sobel operator in order to filter out the generated false vehicle and retain the real vehicle. The camera internal parameters are calibrated by means of Zhang Zhengyou plane calibration method, and then the single vision ranging method and the binocular parallax ranging method are used to measure the real time range of the vehicle in front. The software of front vehicle detection and ranging system based on machine vision is developed on the platform of Lab VIEW. The computer vision library of Lab VIEW is used to complete the development of each module of the system. The experimental results show that the system can accurately detect the front vehicle, and the distance between the two vehicles can be measured within 10 meters recently, and the farthest distance can be measured to about 50 meters. Meet the requirements of safety vehicle distance of 50 meters.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495;U463.6;TP391.41

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本文編號(hào)：1944469