本文應用影像處理技術於偵測與辨識各種顏色及各種形狀的交通標誌，利用電腦視覺系統提供道路上交通標誌相關資訊給駕駛者，以增加駕駛的安全性。研究中主要著重於發展在複雜的背景與條件當中，仍能正常運作的交通標誌偵測辨識系統。在偵測系統中，使用HSV彩色模型以減少光源與天候對系統的干擾；提出一強健性的形狀判別方法減少交通標誌外框不完整的影響；定義出兩種不同的彩色像素分別供偵測與色彩重建時使用；利用灰階像素計算灰階值變異數並加上統計式門檻值決定法成功的重建黑白像素；對標誌做初步分類並根據分類結果裁減中間資訊部分以利之後辨識。在辨識系統中，將交通標誌分為圖形標誌、文字標誌、數字標誌三類並分別開發專屬的辨識系統。實驗中總共輸入83張照片共102個交通標誌，偵測系統偵測出其中的87個交通標誌；在87個標誌中辨識系統正確辨識出77個交通標誌。結果顯示，在不同天候狀況下（晴天、陰天、雨天等）以及遮蔽狀況不嚴重時，偵測系統均能正確的偵測出交通標誌。而辨識系統也因辨識前先行初步分類以及將圖形、文字、數字分開辨識而達到不錯的效果。

This thesis applies image processing techniques to detect and recognize various colors and shapes of traffic signs. This information of road signs, provided to drivers in a computer-based vision system, is helpful for driving safety. In this study, the traffic sign detection and recognition system which is able to work well in complicated backgrounds and conditions is developed. We use HSV color model to reduce the disturbance of illumination and weather, and present a robust method to identify shapes of incomplete traffic signs. Two different color-pixel definitions are used for color detection and recognition, and calculation of gray-level variance, together with the Otsu statistical threshold selecting method, determines the black and white pixels. In order to increase recognition efficiency, we use the preliminary classification and cut part of the sign information. Furthermore, we divide traffic signs into three categories, which are graph signs, text signs and numeral signs, and they have their own recognition systems. In the experiment, 87 images include 102 traffic signs to discover 87 traffic signs based on detection system, which are used by the recognition system to identify accurately 77 traffic signs. The results demonstrate that the detection system can detect traffic signs under different weather conditions and slight cover conditions, and the recognition system has good performance.

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