簡易檢索 / 詳目顯示

回結果列表
研究生： 游重賢
Chung-Hsien, Yu
論文名稱： 採用改良式橢圓形感興趣區域與Sobel邊緣偵測之低躁動車道線偵測
A Low-vibration Lane Detection Using an Improved Elliptical ROI and Sobel Edge Detection
指導教授： 蘇崇彥
Su, Chung-Yen
學位類別： 碩士
Master
系所名稱： 電機工程學系
Department of Electrical Engineering
論文出版年： 2013
畢業學年度： 101
語文別： 中文
論文頁數： 66
中文關鍵詞： 車道線偵測 、邊緣偵測 、橢圓形感興趣區域 、霍夫轉換
英文關鍵詞： Lane detection, Edge detection, Elliptical ROI, Hough transform
論文種類： 學術論文
相關次數： 點閱：146  下載：16 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 本論文使用垂直方向的Sobel邊緣偵測和雜訊濾波器來改善車道線追蹤時所產生的躁動問題。因為改善了躁動問題，準確率也獲得了改善。此外透過縮減影像空間處理的區域和合理的縮減橢圓形感興趣區域的大小，進而有效地提升了車道線偵測系統的處理速度。

    透過實驗分析比較結果，在晴天、夜晚與雨天的情況底下皆能獲得有效的改善。實驗使用640 × 480大小的影片測試，每秒約可處理55~60張畫面，提升了約71%左右，整體的準確率方面也由原先的96.19%，提升至97.07%。

    In the paper, we use the vertical Sobel edge detection and a noise filter to solve the problem of by pulse for the tracking mode of lane detection. Since the problem of by pulse is effectively solved, the lane detection accuracy is increased. Furthermore, we can effectively improve the processing speed of lane detection system, by reducing the image space and the elliptical ROI size.

    In experiment results, the proposed method can effectively solve the problem of by pulse in daytime, night and rain situations. The test video size is 640 × 480. The processing speed is about 55~60 frames per second. Compared with the previous method, the proposed algorithm can speed the processing of frames up to 71%, and the total accuracy is increased from 96.19% to 97.07%.

    中文摘要..................................................................i 英文摘要..................................................................ii 誌 謝..................................................................iii 目 錄..................................................................iv 圖 目 錄..................................................................vi 表 目 錄.................................................................viii 第一章 緒論............................................................- 1 - 1.1. 研究背景與動機....................................................- 1 - 1.2. 文獻回顧.........................................................- 3 - 1.3. 研究目的.........................................................- 5 - 第二章 影像處理.........................................................- 7 - 2.1. 邊緣偵測.........................................................- 7 - 2.1.1. Canny邊緣偵測....................................................- 7 - 2.1.2. Sobel邊緣偵測....................................................- 9 - 2.2. 霍夫轉換.........................................................- 10 - 第三章 適應性橢圓形ROI車道線偵測演算法......................................- 13 - 3.1. 研究作法.........................................................- 13 - 3.2. 前處理...........................................................- 15 - 3.3. 濾除雜訊遮罩......................................................- 19 - 3.4. 車道線偵測........................................................- 21 - 3.4.1. 初始畫面模式......................................................- 21 - 3.4.2. 快速追蹤模式......................................................- 23 - 3.5. 適應性的橢圓形ROI大小設定...........................................- 25 - 3.6. 限制影像空間中處理的範圍............................................- 28 - 3.7. 躁動問題的改善....................................................- 29 - 第四章 數值分析與實驗結果.................................................- 32 - 4.1. 實驗器材與環境架設.................................................- 32 - 4.2. 雜訊濾波器分析與比較................................................- 33 - 4.3. 躁動分析與討論.....................................................- 35 - 4.4. 橢圓形ROI縮減分析與比較.............................................- 38 - 4.5. 影像空間縮減分析...................................................- 41 - 4.6. 白天車道線偵測結果比較..............................................- 43 - 4.7. 夜間車道線偵測結果比較..............................................- 49 - 4.8. 雨天車道線偵測結果比較..............................................- 53 - 4.9. 實驗結果總結......................................................- 57 - 第五章 結論與未來展望....................................................- 60 - 參考文獻.................................................................- 61 - 自 傳.................................................................- 65 - 學術成就.................................................................- 66 -

    [1] 行政院衛生署 http://www.doh.gov.tw/cht2006/index_populace.aspx
    [2] 內政部警政署 http://www.npa.gov.tw/NPAGip/wSite
    [3] M. Bertozzi and A. Broggi, “Real-time lane and obstacle detection on the GOLD system,” Proc. IEEE Intelligent Vehicles Symposium, Tokyo, Japan, Sep.18-20, pp.213-218, 1996.
    [4] S.-Y. Chen and J.-W. Hsieh, “Edge-Based Lane Change Detection and its Application to Suspicious Driving Behavior Analysis,” International Conference Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP), pp.415–418, Nov. 2007.
    [5] C. Lipski, B. Scholz, K. Berger, C. Linz, and T. Stich, “A Fast and Robust Approach to Lane Marking Detection and Lane Tracking,” IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 57–60, March 2008.
    [6] B.-F. Wu, W.-H. Chen, C.-W. Chang, C.-J. Chen, and M.-W. Chung, “A New Vehicle Detection with Distance Estimation for Lane Change Warning Systems,” IEEE Intelligent Vehicles Symposium, pp. 698–703, June 2007.
    [7] Y.-U. Yim and S.-Y. Oh, “Three-Feature Based Automatic Lane Detection Algorithm (TFALDA) for Autonomous Driving,” IEEE Trans. Intelligent Transportation Systems, pp.219–225, 2003.
    [8] C.-C. Wang, S.-S. Huang, and L.-C. Fu, “Driver assistance system for lane detection and vehicle recognition with night vision,” International Conference Intelligent Robots and Systems (IROS), pp.3530-3535, Aug. 2005.
    [9] A.A. Assidiq, O.O. Khalifa, M.R. Islam, and S. Khan, “Real Time Lane Detection for Autonomous Vehicles,” International Conference Computer and Communication Engineering (ICCCE), pp. 82–88, May 2008.
    [10] C.R. Jung and C.R. Kelber, “Lane following and lane departure using a linear-parabolic model,” Image and Vision Computing, vol. 23, no.13, pp. 1192-1202, Nov. 2005.
    [11] Qing Li, Nanning Zheng, and Hong Cheng, “Springrobot: a prototype autonomous vehicle and its algorithms for lane detection,” IEEE Trans. on Intelligent Transportation Systems, vol. 5, No. 4, Dec. 2004.
    [12] C. D’Cruz and J.J. Zou, “Lane Detection for Driver Assistance and Intelligent Vehicle Applications,” International Symposium Communications and Information Technologies, pp. 1291–1296, Oct. 2007.
    [13] Yue Wang, Eam Khwang Teoh, and Dinggang Shen, “Lane detection and tracking using B-Snake,” Image and Vision Computing, vol.22, pp. 269-280, 2004.
    [14] Haiping Lin, Suhong Ko, Wang Shi, Yeongim Kim, and Hyongsuk Kim, “Lane departure identification on Highway with searching the region of interest on Hough space,” 2007 ICCAS ’07 International Conference on Control, Automation and Systems, pp. 1088-1091, 2007.
    [15] Jianfeng Wang, Ye Wu, Zehua Liang, and Yuanjun Xi, “Lane Detection Based on Random Hough Transform on Region of Interesting,” 2010 IEEE International Conference on Information and Automation (ICIA), pp. 1735-1740, 2010.
    [16] Feng You, Rong-ben Wang and Rong-hui Zhang, “Based on Digital Image Lane Edge Detection and Tracking under Structure Environment for Autonomous Vehicle,” IEEE International Conference on Automation and Logistics, pp.1310-1314, 2007.
    [17] Bang-Gui Zheng, Bing-Xiang Tian, Jian-Min Duan and De-Zhi Gao, “Automatic Detection Technique of Preceding Lane and Vehicle,” Proceedings of the IEEE International Conference on Automation and Logistics Qingdao, pp. 1370-1375, 2008.
    [18] Pei-Yung Hsiao, Chun-Wei Yeh, Shih-Shinh Huang, and Li-Chen Fu, “A Portable Vision-Based Real-Time Lane Departure Warning System: Day and Night,” IEEE Transactions on Vehicular Technology, Vol. 58, No. 4, pp. 2089-2094, 2009.
    [19] Qing Lin, Youngjoon Han, and Hernsoo Hahn, “Real-time Lane Detection Based on Extended Edge-linking Algorithm,” 2010 Second International Conference on Computer Research and Development, pp. 725-730, 2010.
    [20] Cheng-Jian Lin, Jyun-Guo Wang, Shyi-Ming Chen, and Chi-Yung Lee, “Design of a Lane Detection and Departure Warning System Using Functional-Link-Based Neuro-fuzzy Networks,” 2010 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1-7, 2010.
    [21] Seonyoung Lee, Haengseon Son, and Kyungwon Min, “Implementation of Lane Detection System using Optimized Hough Transform Circuit,” 2010 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), pp. 406-409, 2010.
    [22] Yu-Chi Leng, and Chieh-Li Chen, “Vision-Based Lane Departure Detection System in Urban Traffic Scenes,” 2010 11th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 1870-1875, 2010.
    [23] Keyou Guo, Na Li, and Mo Zhang, “Lane Detection Based on the Random Sample Consensus,” 2011 International Conference on Information Technology, Computer Engineering and Management Sciences (ICM), vol. 3, pp. 38-41, Sept. 2011.
    [24] Hsiu-Yuan Fan, “An Effective Detection Based on Elliptical ROI and Limited Parameters in Hough Space,” Journal of Engineering, National Chung Hsing University, Vol. 23, No. 1, pp. 1-12, 2012.
    [25] 霍夫轉換參閱自維基百科全書 http://en.wikipedia.org/wiki/Hough_transform
    [26] R. K. Satzoda, S. Suchitra, and T. Srikanthan, “Parallelizing the Hough Transform Computation,” Signal Processing Letters, IEEE, vol. 15, pp. 297-300, 2008.
    [27] Ravi Kumar Satzoda, Suchitra Sathyanarayana, Thambipillai Srikanthan, “Hierarchical Additive Hough Transform for Lane Detection,” IEEE EMBEDDED SYSTEMS LETTERS, vol. 2, pp. 23-26, June 2010.

    QR CODE