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研究生:蔡駿鋼
研究生(外文):TSAI, CHUN-KANG
論文名稱:低運算量車道偏移警示駕駛輔助系統之實現
論文名稱(外文):The Implementation of Low-Computation Lane Departure Warning for Driving Assistant System
指導教授:許佳興許佳興引用關係
指導教授(外文):SHEU, JIA-SHING
口試委員:許佳興李俊賢何健鵬
口試委員(外文):SHEU, JIA-SHINGLEE, JIN-SHYANHO, CHIEN-PENG
口試日期:2021-07-01
學位類別:碩士
校院名稱:國立臺北教育大學
系所名稱:資訊科學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:77
中文關鍵詞:嵌入式系統電腦視覺車道偵測車道偏移警示系統Code-11編碼
外文關鍵詞:Embedded SystemComputer VisionLand DetectionLane Shift Warning SystemsCode-11 Encode
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本文開發了一種簡單的基於嵌入式圖像識別技術的自動駕駛系統。該系統結合了樹莓派、相機模組和OpenCV,並且可以很容易地設置。第一步,相機模組將採集到的RGB車道影像進行比HSV色彩過濾轉換、灰階化、中值濾波、二值化、侵蝕和膨脹等去噪聲處理。接下來將進行虛擬水平橫線交點偵測,在此步驟中,使用平行於x軸、多條固定範圍、固定間隔的線來檢測左右車道線。這樣就可以得到左右車道線和水平線的交點。取其中四個交點的坐標,並透過公式轉換為左右車道的斜率、做出直線延伸至左右車道的交點。最後,通過發現左右車道的斜率變化和車道交叉點(VP)的偏移量來檢測偏移。當角度變化劇烈時,偏離警告可以對駕駛員起到警示作用。
This paper develops a simple automatic driving system based on embedded image recognition technology. The system combines Raspberry Pi, camera module and OpenCV, and can be easily set up. In the first step, the camera module will carry out noise removal processing such as HSV color filtering conversion, grayscale, median filtering, binarization, erosion and expansion of the collected RGB lane images. Next, the virtual horizontal and horizontal intersection detection is performed. In this step, the left and right lane lines are detected using multiple lines of fixed range and fixed intervals parallel to the X axis. This gives you the intersection of the left and right lane lines and the horizontal line. Take the coordinates of four intersections, and convert them into the slope of left and right lanes through the formula, and make a straight line extending to the intersection of left and right lanes. Finally, the offset is detected by finding the slope change of the left and right lanes and the offset of the lane intersection point (VP). Deviation warning can warn the driver when the Angle changes dramatically.
摘要 i
Abstract ii
目錄 iii
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 問題與陳述 2
1.4 研究方法及貢獻 2
1.5 論文架構 3
第二章 文獻探討與相關技術 4
2.1 文獻回顧 4
2.1.1 感測元件文獻回顧 4
2.1.2 車道偵測演算法回顧 6
2.2 色彩空間 7
2.2.1 RGB色彩空間 8
2.2.2 HSV色彩空間 9
2.2.3 YCbCr色彩空間 11
2.3 降解析度 12
2.4 去雜訊 13
2.4.1 高斯濾波器 13
2.4.2 中值濾波器 14
2.5 邊緣偵測 15
2.5.1 Sobel邊緣偵測 15
2.5.2 Canny邊緣偵測 16
2.6 大津二值化 18
2.7 形態學 19
2.7.1 侵蝕 20
2.7.2 膨脹 20
2.7.3 關閉 21
2.7.4 開啟 21
2.8 相機校準與透視變換 22
2.9 感興趣區域提取 24
2.10霍夫變換 24
2.11 Code-11 25
第三章 系統方法 27
3.1 系統方法論 27
3.1.1 IDEF0階層式系統方法 27
3.2 影像處理 30
3.2.1 RGB車道影像擷取 31
3.2.2 HSV色彩過濾 32
3.2.3 灰階化 34
3.2.4 中值濾波器 35
3.2.5 二值化 36
3.2.6 固定式遮罩 37
3.2.7 侵蝕 38
3.2.8 膨脹 38
3.2.9 直線交點偵測 39
3.2.10 自適應遮罩 40
3.2.11 車道偏移警示 41
3.3 Code-11 條碼偵測 42
第四章 實驗平台 43
4.1 系統開發環境 43
4.2 實驗環境-硬體 44
4.2.1 樹莓派 44
4.2.2 樹莓派相機模組 45
4.2.3 樹莓派觸控面板 46
4.3 實驗環境-軟體 47
4.3.1 樹莓派作業系統 47
4.3.2 圖形使用者介面 47
第五章 實驗結果 48
5.1 實驗環境 48
5.2 實驗流程 48
5.3 影像處理實驗結果 49
5.3.1 RGB車道影像擷取結果 49
5.3.2 HSV色彩過濾結果 50
5.3.3 灰階化結果 51
5.3.4 中值濾波結果 52
5.3.5 大津二值化結果 53
5.3.6 參數二值化結果 54
5.3.7 固定式遮罩結果 55
5.3.8 侵蝕運算結果 56
5.3.9 膨脹運算結果 57
5.3.10 直線交點偵測結果 58
5.3.11 自適應遮罩結果 67
5.4 條碼偵測結果 70
第六章 結論與未來展望 71
6.1 結論 71
6.2 未來展望 72
參考文獻 73

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