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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/349


    Title: 基於單攝影機影像處理之前車距離估測;Distance Estimation of Front Vehicle Based on Single Image
    Authors: 陳政德;Chen, Cheng-De
    Contributors: 電機工程研究所
    Keywords: 先進駕駛輔助系統;車道線偵測;前車偵測;前車距離估測;Advanced driver assistance system;lane detection;vehicle detection;distance estimation of front vehicle
    Date: 2016
    Issue Date: 2019-07-17
    Publisher: 電機工程研究所
    Abstract: 先進駕駛輔助系統 (Advanced driver assistance system, ADAS) 是近年來各大車廠亟欲開發的一項技術,常見的輔助系統包括車道偏移警示 (LDW) ,前車碰撞警示 (FCW) 以及主動式車距控制巡航系統 (ACC) 等應用。先進駕駛輔助系統的目的,是在駕駛可能發生危險情況下,發出警示與輔助駕駛,避免意外發生以達到降低交通事故的發生率。 本論文針對車輛行駛的當前車道,利用車輛底部陰影特徵與車輛底部具強烈邊緣特性來偵測前方車輛,最後提出基於車道寬度與基於車輛底部陰影位置的演算法來計算與前車距離。首先依據偵測出來的車道線定義感興趣區域,對區域內進行車輛偵測。由於車底陰影在日間之行車影像上是一個穩定的特徵,並不會受到車輛顏色影響,且車底陰影的顏色灰階值通常小於當前車道顏色灰階值。因此我們統計車道區域範圍內的顏色灰階值,當作車底陰影的參考閥值,同時利用不同於偵測車道線的邊緣閥值,以較低的邊緣閥值偵測車輛邊緣。在偵測車輛時,以車輛邊緣特徵為主、車底陰影特徵為輔,以邊緣像素比例以及暗點像素比例判斷前方車輛底部位置。本論文最後統計車輛偵測在不同情境下的偵測結果,本系統在前方有車輛時的正確偵測率可達 95% 以上。
    The Advanced driver assistance system (ADAS) is a technology that the most automobile manufactures has been developed in recent years. The Lane Departure Warning System, the Forward Collision Warning System, and the Adaptive Cruise Control System are the common assistant System. In our thesis, we use the characteristic of the shadow of vehicle and the strong edge of the vehicle to detect the vehicle on current lane. Finally, we propose an algorithm to calculate the distance of front vehicle based on the width of the lane and the position of the shadow of vehicle. At first, we detect the feature of vehicle that according to the ROI, which is depended on the detected lane. Since the shadow of vehicle is a stable feature in daytime’s image, that is not affected by the color of vehicle’s body, and the gray level of the vehicle’s shadow is usually smaller than the gray level of the lane. Therefore, we calculate the gray level of the ROI as the referential thresholding, that use to detect the shadow of vehicle. In the meantime, we detect the edge of vehicle using the lower thresholding, which is different from the thresholding that detected to the lane. When detects the vehicle, the feature of vehicle’s edge is the first consideration, and the shadow of vehicle is the secondary. And we use the ratio of the edge’s pixel and the ratio of the dark pixel to get the position of the vehicle. In the end of the thesis, we statistics the ratio of accuracy of the detect vehicle under different situations, has up to 95%.
    Appears in Collections:[電機工程研究所] 學位論文

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