本文選題：微型無人機　切入點：交通事故　出處：《中國礦業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：交通事故快速處理是智能交通發(fā)展的重要領(lǐng)域之一。隨著國民經(jīng)濟(jì)的快速發(fā)展,機動車保有量的快速增加,交通事故越發(fā)頻繁。傳統(tǒng)的交通事故量測方式主要依靠人工量測,作業(yè)效率低,較長時間的量測極可能造成交通擁堵,同時量測結(jié)果純粹靠手工記錄,勘測精度不高,也無法對事故場景二次驗證。隨著各類新型測量儀器以及數(shù)碼照相機應(yīng)用于交通事故現(xiàn)場勘測,在一定程度上緩解了現(xiàn)場人員的壓力,但是仍然避免不了低效率的作業(yè)模式。因此,對交通事故現(xiàn)場勘測新技術(shù)的研究具有重要的理論價值和實際意義。針對目前火熱的無人機市場,本文對無人機低空影像下的道路信息檢測方案進(jìn)行研究,對影像中的車輛及車道線進(jìn)行目標(biāo)識別,同時基于識別結(jié)果完成標(biāo)定和圖像重繪工作。(1)提出一種基于彩色特征點的車道線檢測算子。在分析SUSAN角點檢測原理后,提出一種改進(jìn)的具有針對性的角點檢測算法,可以對具有獨立明顯特征的物體進(jìn)行檢測,并對檢測結(jié)果進(jìn)行針對性的聚類,達(dá)到車道線檢測的效果。實驗結(jié)果表明在影像較好的情況下,這是一種精度較高的精細(xì)化提取方法。(2)提出一種利用車道線寬度進(jìn)行標(biāo)定的自標(biāo)定方法。這種方法可以在道路存在行車線的情況下對影像進(jìn)行標(biāo)定,實現(xiàn)圖像尺度與現(xiàn)實尺度的轉(zhuǎn)換,實驗結(jié)果證明這種標(biāo)定方法的精度比較好,可以滿足后續(xù)測量工作的要求。(3)對影像中車輛目標(biāo)的光學(xué)特征進(jìn)行分析,實現(xiàn)了基于多特征融合的車輛檢測方法的研究,在分析了常見的各類小型汽車后,構(gòu)建了一種方便表達(dá)的車輛拓?fù)淠Ｐ?并與車輛檢測的結(jié)果進(jìn)行匹配,基本實現(xiàn)了影像中車輛的檢測及矢量化工作。最后,本文利用軟件實現(xiàn)了影像中道路信息重繪的一系列功能,基本實現(xiàn)了基于單幅影像的道路信息檢測的目的。
[Abstract]:The rapid handling of traffic accidents is one of the important fields in the development of intelligent transportation. With the rapid development of national economy and the rapid increase of vehicle ownership, traffic accidents become more and more frequent. The operation efficiency is low, the measurement pole for a long time may cause traffic jam, at the same time, the result of measurement is recorded purely by hand, and the precision of survey is not high. With the application of various kinds of new measuring instruments and digital cameras to traffic accident scene investigation, the pressure of personnel on the scene has been alleviated to a certain extent. But it still can't avoid the inefficient operation mode. Therefore, the research on the new technology of traffic accident scene survey has important theoretical value and practical significance. In this paper, the road information detection scheme of UAV under low altitude image is studied, and the vehicle and lane line in the image are identified. At the same time, a lane detection operator based on color feature points is proposed based on calibration and image redrawing based on recognition results. After analyzing the principle of SUSAN corner detection, an improved corner detection algorithm is proposed. It is possible to detect objects with distinct and independent characteristics, and cluster the detection results in order to achieve the effect of lane detection. The experimental results show that the images are better. This is a fine extraction method with high precision. (2) A self-calibration method based on lane width is proposed, which can be used to calibrate the image in the presence of road lane. The experimental results show that the calibration method is accurate and can meet the requirements of subsequent measurement work. (3) the optical characteristics of vehicle targets in the image are analyzed. The research of vehicle detection method based on multi-feature fusion is realized. After analyzing the common small vehicles, a convenient vehicle topology model is constructed and matched with the results of vehicle detection. Finally, this paper realizes a series of functions of road information redrawing in image by software, and basically realizes the purpose of road information detection based on single image.
【學(xué)位授予單位】：中國礦業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U495

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