具有適應性的交通標誌號誌偵測與辨識

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：27

、訪客IP：3.144.124.232

姓名

林盈宏(Ying-Hong Lin) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

具有適應性的交通標誌號誌偵測與辨識
(An adaptive traffic sign and signal detection and recognition)

相關論文

★ 適用於大面積及場景轉換的視訊錯誤隱藏法	★ 虛擬觸覺系統中的力回饋修正與展現
★ 多頻譜衛星影像融合與紅外線影像合成	★ 腹腔鏡膽囊切除手術模擬系統
★ 飛行模擬系統中的動態載入式多重解析度地形模塑	★ 以凌波為基礎的多重解析度地形模塑與貼圖
★ 多重解析度光流分析與深度計算	★ 體積守恆的變形模塑應用於腹腔鏡手術模擬
★ 互動式多重解析度模型編輯技術	★ 以小波轉換為基礎的多重解析度邊線追蹤技術(Wavelet-based multiresolution edge tracking for edge detection)
★ 基於二次式誤差及屬性準則的多重解析度模塑	★ 以整數小波轉換及灰色理論為基礎的漸進式影像壓縮
★ 建立在動態載入多重解析度地形模塑的戰術模擬	★ 以多階分割的空間關係做人臉偵測與特徵擷取
★ 以小波轉換為基礎的影像浮水印與壓縮	★ 外觀守恆及視點相關的多重解析度模塑

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

交通標誌和號誌的設置目的是要提供所有駕駛人關於道路狀況的資訊，以期達到整體交通安全的目標。對於一般駕駛人而言，大約利用八成的精神專注在視覺上，二成的精神使用耳朵注意駕駛環境。為了分擔駕駛人在視覺上的負擔，若有一套交通標誌及號誌自動偵測與辨識系統，將可增加駕駛人行車的安全性及方便性。在本論文的研究中，我們將一部相機架設在車上，用來偵測交通標誌及號誌，並且辨識速限交通標誌的速度來幫助駕駛人避免超速危險的發生。
我們以IHS色彩資料來做為偵測外框為紅色的交通標誌。為了增加偵測率，我們將偵測環境自動區分成白天與夜間兩大類，再分別採取不同的色彩定義進行顏色擷取。接著產生連結區塊，並以這些區塊的形狀、大小、及寬高比進行篩選。篩選完後，將區塊正規到固定大小並用樣板比對方式分成圓形、三角形及半圓形三類。針對限速標誌的內含資訊，採用影像的疊代逼近門檻值法來做二值化處理，並透過形態學的技術得到更正確的限速資訊當作放射狀基底函數網路的輸入來辨識其內容。針對高速公路看板偵測，我們對黃色、綠色及藍色看板在IHS色彩空間上分別定義其顏色，產生連結區塊後，以區塊形狀、大小、及寬高比進行篩選就可以確認其位置。由於看板顏色在高速公路上差異較為大，因此只要做完區塊篩選，就有很高的正確率。
交通號誌的偵測同樣也是以IHS色彩資料來做偵測，我們分別針對紅燈、黃燈及綠燈定義出三組色彩。經由產生連結區塊並針對區塊的形狀、大小、及寬高比進行篩選。我們利用號誌燈的特性對剩餘的區塊進行確認動作。若同時偵測到兩種不同的號誌燈，則比較其面積大小，以面積大者為主。
我們在不同環境下測試我們的系統。交通標誌的偵測在893個含有交通標誌的影像中成功的偵測795個標誌，正確率89%，偵測錯誤的主要原因是由於標誌距離過遠時加上車子行進間的震動無法擷取完整交通標誌外框。限速標誌的辨識在327個測試資料中正確辨識289個資料，正確率有88.3%。交通號誌偵測實驗在339張含有交通號誌的影像中正確偵測311張影像，正確率91.7%。

摘要(英)

The traffic signs and signals are used to provide the traffic information for all drivers and pedestrians. Generally, drivers pay much visual attention to gain the traffic information. The drivers would be more released if there is an automatic traffic sign and signal detector and recognition. In this study, we propose a traffic sign and signal detection and recognition system to aid the driving for safety.
The traffic signs were detected in the IHS color space. To increase the accuracy, the captured images were automatically divided into two cases: day and night. Secondly, the detected color regions were classified into circle, triangle, and semicircle based on the shape, size, and the rate of width and height. The characters in the speed limit sign were extracted and recognized by the radial basis function network.
The traffic signals were also detected in the IHS color space. The extracted color regions were classified by the shape, size, and the rate of width and height to make sure the traffic signals.
The proposed systems were evaluated in several variant environments. The detection rate of traffic signs is 89% with detected 795 signs out of 893 signs. The errors mostly come from the far-distance unclear signs. The recognition rate of speed characters is 88.3%. The detection rate of traffic signal is 91.7%.

關鍵字(中)

★ 號誌
★ 交通標誌

關鍵字(英)

★ traffic sign
★ traffic signal

論文目次

摘要 II
ABSTRACT IV
誌謝 V
目錄 VI
圖目錄 VIII
表目錄 XII
第一章緒論 1
1.1 研究動機 1
1.2系統架構 2
1.3 論文架構 6
第二章相關研究 7
2.1 交通標誌偵測 7
2.2交通標誌形狀判定 9
2.3交通標誌辨識 11
2.4交通號誌偵測 14
第三章交通標誌及號誌偵測 16
3.1 白天與夜間的判定 16
3.2 特定顏色的萃取 20
3.2.1 紅色標誌的萃取 20
3.2.2看板的萃取 22
3.2.3 號誌的萃取 24
3.3 區塊的篩選 26
3.4 標誌形狀的判定 30
3.4.1 區塊正規化 31
3.4.2 樣板比對 31
3.5 交通號誌的確認 35
3.5.1 號誌區塊篩選 35
3.5.2 號誌燈確認 36
第四章交通標誌辨識 38
4.1 交通標誌正規化 38
4.2 交通內容物擷取 39
4.3 型態學 41
4.3.1 侵蝕 41
4.3.2 膨脹 42
4.3.3 斷開及閉合 42
4.4 放射狀基底函數網路訓練 44
第五章實驗 47
5.1 實驗環境 47
5.2 白天夜間判定 48
5.3 交通標誌偵測 52
5.4 交通號誌偵測 56
5.5 IHS與RGB色彩空間比較 60
5.6 限速標誌辨識 63
第六章結論及未來展望 66
6.1 結論 66
6.2 未來展望 66
參考文獻 68

參考文獻

[1] Asakura, T., Y. Aoyagi, and K. Hirose, “Real-time recognition of road traffic sign in moving scene image using new image filter,” IEEE Trans. Industrial Electronics, vol.3, no.6, pp.2207-2212, Oct. 2000.
[2] Broggi, A., P. Cerri, P. Medici, and P. P. Porta, “Real time road signs recognition,” in Proc. 2007 IEEE Intelligent Vehicles Symposium, Jun.13-15, 2007, Istanbul, Turkey, pp.981-986.
[3] Cheng, S. C., Detection of Unstructured Road Boundary and Road Sign Recognition, Master Thesis, Univ. of National Chung Cheng, Chiayi, Taiwan, 2003.
[4] Chung, U. C., J. M. Wang, and S. W. Cheng, “A vision-based traffic light detection system at intersections,” Journal of National Taiwan Normal University, Mathematics, Science and Technology, vol.47, no.1, pp. 67-86, 2002.
[5] Escalera, A. D. L., L. E. Moreno, M. A. Salichs, and J. M. Armingol, “Road traffic sign detection and classification,” IEEE Trans. Industrial Electronics, vol.44, no.6, pp.848-859, Dec. 1997.
[6] Escalera, A. D. L., J. M. Armingol, J. M. Pastor, and F. J. Rodríguez, “Visual sign information extraction and identification by deformable models for intelligent vehicles,” IEEE Trans. Industrial Electronics, vol.5, no.2, pp.57-68, Jun. 2004.
[7] Fang, C. Y., S. W. Chen, and C. S. Fuh, “Road-sign detection and tracking,” IEEE Trans. Vehicular Technology, vol.52, no.5, pp.1329-1341, Sep. 2003.
[8] Franke, U., D. Gavrila, S. Gőrzig, F. Lindner, F. Paetzslld, and C. Wőhler, “Autonomous driving goes downtown,” IEEE Intelligent Systems, vol.13, no.6, pp.40-48, 1998.
[9] García, M. Á., M. Á. Sotelo, and E. M. Gorostiza, “Traffic sign detection in static images using matlab,” IEEE Trans. Emerging Technologies and Factory Automation, vol.2, no.5, pp.212-215, Sep. 2003.
[10] Huang, W. C. and C. H. Wu, “Adaptive color image processing and recognition for varying backgrounds and illumination conditions,” IEEE Trans. Industrial electronics, vol.45, no.2, pp.351-357, Apr. 1998.
[11] Hsien, J. C., Road Sign Detection and Recognition, Master Thesis, Univ. of Yuan-Ze, Chungli, Taiwan, 2003.
[12] Hsu, S. H. and C. L. Huang, “Road sign detection and recognition using matching pursuit method,” Image and Vision Computing, vol.19, no.3, pp.119-129, 2001.
[13] Hsueh, S. L., Two-step Road Sign Recognition, Master Thesis, Univ. of Yuan-Ze, Chungli, Taiwan, 2002.
[14] Lee, J. H. and K. H. Jo, “Traffic sign recognition by division of characters and symbols regions,” in Proc. of the 7th Korea-Russia Int. Symp., Ulsan , Korea, Jun.28-July 6, 2003, pp.324-328.
[15] Lindner, F., U. Kressel, and S. Kaelberer, “Robust recognition of traffic signals,” in Proc. of Intelligent Vehicles Symp., Parma, Italy, Jun.14-17, 2004.
[16] Maldonado-Bascón, S., S. Lafuente-Arroyo, P. Gil-Jiménez, H. Gómez-Moreno, and F. López-Ferreras, “Road-sign detection and recognition based on support bector machines,” IEEE Trans. Intelligent Transportation Systems, vol.8, no.2, pp.264-278, June 2007.
[17] Miura, J., T. Kanda, and Y. Shirai, “An active vision system for real-Time traffic sign recognition,” in Proc. of IEEE Intelligent Transportation Systems Conf., Dearbom, Michigan, Oct.1-3, 2000, pp.52-57.
[18] Pacheco, L., J. Batlle, and X. Cufi, ”A new approach to real time traffic sign recognition based on color information,” in Proc. of IEEE Intelligent Vehicles Symp., Paris, France, Oct. 24-26, 1994, pp.339-344.
[19] Pérez, E. and B. Javidi, “Nonlinear distortion-tolerant filters for detection of road signs in background noise,” IEEE Trans. Vehicular Technology, vol.51, no.3, pp.567-576, May, 2002.
[20] Priese, L., J. Klieber, R. Lakmann, V. Rehrmann, and R. Schian, “New results on traffic sign recognition,” in Proc. of IEEE Intelligent Vehicles Symp., Paris, France, Oct. 24-26, 1994, pp.249-254.
[21] Priese, L., R. Lakmann, and V. Lakmann, “Ideogram identification in a realtime traffic sign recognition system,” in Proc. of IEEE Intelligent Vehicles Symp., Detroit, MI, Sep. 25-26, 1995, pp.310-314.
[22] Shaposhnikov, D.G., L. N. Podladchikova, A. V. Golovan, and N. A. Shevtsova, “Road sign recognition by single positioning of space-variant sensor window,” in Proc. 15th Int. Conf. Vision Interface, Calgary, Canada, May, 2002, pp.213-217.
[23] Vitabile, S., A. Gentile, and F. Sorbello, “A neural network based automatic road signs recognizer,” in Proc. IJCNN Int. Joint Conf. Neural Networks, Honolulu, Hawaii, May12-17, 2002, pp.2315-2320.
[24] Zheng, Y. J., W. Ritter, and R. Jamssen, “An adaptive system for traffic sign recognition,” in Proc. Intelligent Vehicles '94 Symposium, Paris, France, Oct. 24-26, 1994, pp.165-170.
[25] 蘇木春, 張孝德, 機器人學習類神經網路,模糊系統以及基因演算法則, 全華科技圖書股份有限公司，2003年12月二版六刷.

指導教授

曾定章(Din-Chang Tseng)

審核日期

2009-7-21

推文