發(fā)布時間：2019-03-31 12:05

【摘要】：隨著我國社會經(jīng)濟(jì)的迅速發(fā)展,我國汽車保有量大大增加,但與此同時交通事故的發(fā)生率也逐漸上升。高級駕駛輔助系統(tǒng)(ADAS)是解決交通安全問題的重要手段之一,成為研究學(xué)者們所關(guān)注的重要研究課題。目標(biāo)檢測算法作為高級駕駛輔助系統(tǒng)中的關(guān)鍵技術(shù)之一,近年來有價值的研究成果層出不窮,Subcat,RCNN,Faster-RCNN,YOLO等目標(biāo)檢測算法在簡單場景下有著不錯的表現(xiàn),但是若將這些檢測算法應(yīng)用于實際的交通場景仍存在一定的局限性。本文對復(fù)雜交通場景下駕駛輔助系統(tǒng)的實際應(yīng)用問題進(jìn)行了研究,提出了提高現(xiàn)有目標(biāo)檢測算法精度的優(yōu)化方案。論文的主要工作如下:1.分析了現(xiàn)有目標(biāo)檢測算法在復(fù)雜交通場景中誤檢增多問題的成因,本文根據(jù)攝像機(jī)成像原理,提出了利用幾何約束模型去除誤檢的方法。2.針對現(xiàn)有目標(biāo)檢測算法中出現(xiàn)的漏檢和檢測位置不準(zhǔn)確問題,本文提出了基于條件隨機(jī)場(CRF)的連續(xù)運動信息融合模型,進(jìn)而提高了目標(biāo)檢測算法的性能。3.通過對比實驗驗證了本文提出的目標(biāo)檢測優(yōu)化算法的有效性。實驗結(jié)果顯示在不同的復(fù)雜路況場景下,本文提出的目標(biāo)檢測優(yōu)化算法仍具有可靠性。本文提出的優(yōu)化算法可以與現(xiàn)有的多種目標(biāo)檢測算法相結(jié)合,為復(fù)雜交通場景下的目標(biāo)檢測問題提供了新思路,推進(jìn)了自動駕駛領(lǐng)域的研發(fā)進(jìn)程。
[Abstract]:With the rapid development of China's social economy, the number of cars in our country increases greatly, but at the same time, the incidence of traffic accidents is also increasing gradually. Advanced driving Assistance system (ADAS) is one of the important means to solve the traffic safety problem, which has become an important research topic concerned by scholars. Target detection algorithm is one of the key technologies in the advanced driving assistant system. In recent years, valuable research results emerge one after another. Target detection algorithms such as Subcat,RCNN,Faster-RCNN,YOLO have a good performance in simple scenes. However, if these detection algorithms are applied to the actual traffic scene, there are still some limitations. In this paper, the practical application of the driving assistant system in complex traffic scene is studied, and the optimization scheme to improve the accuracy of the existing target detection algorithms is proposed. The main work of this paper is as follows: 1. Based on the principle of camera imaging, this paper puts forward a method to remove false detection by geometric constraint model. 2.2.Based on the principle of camera imaging, this paper proposes a method to remove false detection by geometric constraint model. In this paper, a continuous motion information fusion model based on conditional random field (CRF) is proposed to improve the performance of target detection algorithms. 3. The effectiveness of the proposed optimization algorithm for target detection is verified by comparison experiments. The experimental results show that the target detection optimization algorithm proposed in this paper is still reliable under different complex road conditions. The optimization algorithm proposed in this paper can be combined with a variety of existing target detection algorithms, which provides a new idea for target detection in complex traffic scenarios and advances the research and development process in the field of autopilot.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U463.6;TP391.41

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