臺灣博碩士論文加值系統

由於現代化運輸工具技術的不斷精進，使得加強感應器對周遭環境的偵測，以提供駕駛者充分資訊，進而建構一個迅速、安全和方便的駕駛環境已非遙不可及的夢想。
本論文即在提出一自動化的交通號誌辨識系統，以提供充分的訊息，讓駕駛者可正確判斷道路環境，進而協助智慧型運輸系統的建置完成。所提方法可適用於固定式或移動式攝影機，故有其方便性及實用性。
所提方法主要分為三個階段:交通號誌的偵測、擷取及分類。這三階段的設計原理分別是根基於色彩資訊、區塊資訊和幾何特性。在偵測階段，首先將色彩空間RGB轉為HSI，以偵測出具有號誌特定色彩的區塊，接著利用形態學的方法補強區塊的缺損及去除雜訊。在擷取階段，主要是利用區塊標記技巧偵測出號誌可能的位置，接著使用邊緣偵測法將區塊邊緣特徵擷取出來，以利後續分類階段利用號誌幾何外型特徵達成分類目的，亦即辨識出圓形號誌燈和箭頭號誌燈。
最後，經由眾多實驗證實所提方法確實有效且可行。

Advanced technology improves the capabilities of modern vehicles. The innovations of senor-based systems support surrounding survey of the vehicle and display relevant information to the driver. Thus, construction of a safety, convenient and efficiency driving environment can be achieved.
This paper is to propose an automatic traffic light recognition system so that car drivers have sufficient information to make a correct decision. This in turn facilitates the construction of ITS (Intelligent Transportation System). The proposed method can be applied not only to fixed camera but also to movable camera.
Our method consists of three phases: traffic light detection, extraction and classification. The three phases are based on color information, region information and geometric and appearance constraints. In the detection stage, the RGB color space is first converted into HSI color space to detect those regions with specific colors of traffic lights. The morphology technology is then employed to remove hole and noise. In the extraction stage, region labeling is involved to detect candidate regions of traffic lights. Border detection is then employed to obtain region border. In the classification stage, geometric and appearance constrains are derived respectively from traffic light shape and color and used for classification. In this study, traffic lights of circle and arrow shape can both be coped with.
Various experiments have been conducted to demonstrate the effectiveness and practicability of proposed method.

Chapter 1 Introduction
1.1 Motivation
1.2 Problem analysis
1.3 Survey of related studies
1.4 Organization of the thesis
Chapter 2 System Overview
2.1 Systematic flowchart
2.2 Systematic structure
2.3 Image capture
Chapter 3 Traffic Light Detection Using Color Information
3.1 HSI color space
3.2 Morphology
Chapter 4 Traffic Light Extraction Using Region Information
4.1 Region labeling
4.2 Border detection
Chapter 5 Traffic Light Classification Using Geometric and Appearance Constraints
5.1 Circle characteristics
5.1.1 Shape characteristics of circle
5.1.2 Color characteristics of circle
5.2 Arrow characteristics
5.3 Algorithm of traffic light classification
Chapter 6 Environment Adaptability
6.1 Variation in numbers of lights
6.2 Variation in types of lights
6.3 Variation in time duration
Chapter 7 Experimental Results
7.1 Environment and equipment
7.2 Test images
7.3 Results of circle traffic lights
7.4 Results of arrow traffic lights
7.5 Results of adaptation to environment variations
7.6 Performance evaluation
Chapter 8 Conclusions and Future Work
8.1 Conclusions
8.2 Future work
References

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