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研究生:林士然
研究生(外文):Shih-Jan Lin
論文名稱:影像視覺於車輛前方碰撞防護之研究
論文名稱(外文):A Study Of Vehicle Forward Collision Prevention System With Visual Image-Based Detection
指導教授:林靖國林靖國引用關係
指導教授(外文):Ching-Kuo Lin
口試委員:陳昭榮黃忠仁
口試日期:2012-06-18
學位類別:碩士
校院名稱:華梵大學
系所名稱:機電工程學系博碩專班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:140
中文關鍵詞:車輛偵測
外文關鍵詞:Vehicle Detection
相關次數:
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機器視覺近年來已在各式各樣的領域中占有重要地位並大量運用在工業、醫學以及家電用品等,隨著科技的進步與硬體上的改良,逐漸運用於車輛安全輔助系統上。

本文應用機器視覺原理及影像處理技術偵測全天候前方車輛,系統使用彩色影像的CCD或CMOS攝影機擷取車輛前方影像後進行影像處理,並配合演算法與GPS和車上感測器搜尋標的物,並於數位訊號處理器(TI DM642)或筆記型電腦加以分析、處理,並比較其效能。

系統透過車道模型建立基本參數設定偵測範圍以尋找車輛候選物件位置,並經由特徵萃取判別是否為車輛以推算車距。為了克服各種天候狀況如:強烈陽光、陰影、道路標字、路面反光、路面積水、雨水及雨刷等影響,加入各種特徵條件組合判斷,使系統對於各種環境有相當高的適應性。同時本論文使用GPS與車上感測器資訊搭配道路設計規範,提升系統偵測能力與可靠性,因此不論在高速公路、市區、隧道、晴天、陰天、雨天、傍晚、夜間場景下系統均可運作,達到車輛輔助的效果以提升行車安全的目標。

During the recent several years, machine vision has played an important role in various bound and it has been widely applied in general industry, medical industry, and home appliances etc. with the advancement of technology level and the improvement on the hardware, machine vision gradually can be applied to vehicle auxiliary safety system.

To detect forward vehicle in all-weather condition, the system apply to machine vision principle and image processing technology. And using color image CCD or CMOS camera to capture the image of forward vehicle image and then perform image processing. And use algorithm to detect object. And used digital signal processing device TI-DM642 or notebook to analyze, to process and to compare its efficiency.

First, the system through the lane model to establish the basic parameters to set the ROI(Region Of Interest) to find the location of the vehicle candidate object, and then through feature extraction method to distinguish true vehicle or not to calculate the distance between vehicles. In order to overcome the influence of all kinds of weather conditions such as: strong sunshine, shadows, road marking , light reflection on the road, road water, rain and windshield wipers, etc., the combination of conditions on the various features is added on the system. So it obtain very high adaptability of the system for a variety of environments.

Therefore, not only on the highways, but also in urban areas, tunnels, sunny day, cloudy day, rainy day, even in the evening, night scenes, the system may be operated to achieve the effect of vehicle auxiliary to enhance traffic safety goals.

誌謝 I
摘要 II
ABSTRACT III
目錄 IV
表目錄 X
圖目錄 XII
第一章、緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 3
1.3 系統功能 5
1.4 論文貢獻 5
第二章、道路設計規範與應用 6
2.1 市區道路設計規範 6
2.2 公路道路設計規範 9
2.3 設計規範應用 13
2.3.1 通訊協定 13
第三章、影像視覺與數位影像 17
3.1 數位影像基礎 17
3.2 數位影像擷取 18
3.3 影像的感應和量化 19
3.4 影像前處理 20
3.4.1 邊緣輪廓 20
3.4.2 多值化 24
第四章、路況偵測與安全防護 26
4.1 複合式車道線偵測 26
4.1.1 偵測流程 26
4.1.1.1 外部資訊應用流程 28
4.1.2 模型建立 29
4.1.2.1 攝影機模型與透視轉換 29
4.1.2.2 攝影機模型與道路傾角修正 32
4.1.2.3 車道線模型 34
4.1.2.3.1最小平方法 37
4.1.3 車道線偵測區域設定 40
4.1.3.1 雙車道線偵測區域設定 40
4.1.3.2 單車道線偵測區域設定 42
4.1.4 適應性車道標線偵測 44
4.1.4.1 邊緣輪廓特徵 44
4.1.4.2 灰階差異特徵 46
4.1.4.3 車道標線寬度特徵 48
4.1.4.4 車道標線偵測流程 49
4.1.4.5 車道標線偵測錯誤排除 51
4.1.5 車道判別 52
4.2 全天候車輛偵測 53
4.2.1 偵測流程 53
4.2.2 偵測區域設定 54
4.2.3 多層次車輛候選物件框選 56
4.2.3.1 尋找車輛底部 57
4.2.3.1.1連續水平邊緣特徵 57
4.2.3.1.2連續水平陰影特徵 59
4.2.3.1.3車輛腳印特徵 60
4.2.3.1.4車尾燈特徵 63
4.2.3.2 尋找車輛左右邊界 65
4.2.3.3 車輛候選物件確認 67
4.2.4 車輛影像確認 69
4.2.4.1 車輛特徵萃取 69
4.2.4.1.1垂直邊緣密度 69
4.2.4.1.2水平邊緣密度 70
4.2.4.1.3區塊陰影密度 71
4.2.4.1.4車尾燈特徵數量 72
4.2.4.1.5對稱軸比對 73
4.2.4.2 影像分類 74
4.2.4.2.1日間車輛判斷規則 74
4.2.4.2.2夜間車輛判斷規則 76
4.2.4.2.3雨天車輛判斷規則 78
4.2.4.2.4特殊環境車輛判斷規則 81
4.2.5 車輛追蹤 82
4.2.5.1 設定追蹤範圍 82
4.2.5.2 追蹤終止條件 83
4.3 安全防護 84
4.3.1 車道偏移警示 84
4.3.2 前方車輛碰撞警示 85
第五章、實驗與分析 87
5.1 實驗平台與架設 87
5.1.1 實驗平台 88
5.1.2 攝影機選擇 91
5.1.3 攝影機架設與校正 93
5.2 實驗結果 97
5.2.1 車輛偵測結果 97
5.2.2 車道線偵測結果 101
5.3 實驗分析 106
5.3.1 邊緣輪廓空間濾波器比較 106
5.3.2 外部資訊對系統進出隧道偵測能力的影響 107
5.3.3 外部資訊對車道線偵測能力的影響 108
5.3.4 車輛偵測分析 109
5.3.5 車道線偵測分析 112
第六章、結論與未來發展 114
6.1 結論 114
6.2 未來發展 115
參考文獻 116

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