臺灣博碩士論文加值系統

本論文提出一個應用兩階段分類法於交通標誌偵測與辨識的方法。交通標誌之自動化偵測與辨識除了可以輔助駕駛自動化外，更可以提供智慧型運輸系統應有的即時訊息，以提高自動駕駛的安全性與可信賴度。除此之外，亦可輔助駕駛者注意行車安全，保障駕駛者與行人之安全。本論文主要分偵測階段與辨識階段兩部分：偵測交通標誌階段，透過標誌之幾何特性，運用霍氏轉換之計算、角點之偵測以及投影之方式，找出交通標誌之影像位置，並使用三角形與圓形之幾何特徵去除不相關之背景。辨識階段則利用迴旋積及半徑式函數類神經網路與K-d樹搜尋辨識兩階段做分類辨識分析：首先利用半徑式函數類神經網路進行分群，以期降低辨識錯誤之比率。最後針對半徑式函數類神經網路所區分之每一群，各自利用特徵值切割建立一個K-d樹來進行搜尋辨識，K-d樹除了可以搜尋辨識外，亦可以修正在半徑式函數類神經網路中分群錯誤之圖像。實驗結果顯示，大部分的交通標誌均可以成功的偵測與辨識，辨識率可到達95.5%，同時對於特殊狀況影響之標誌亦均能有效地偵測與辨識。本論文所提之方法將有助於日後智慧型輔助運輸系統之發展，進而提供即時有效之道路標誌輔助訊息。

This study propose a road sign detection and recognition method using two-stage classification method. The automation of road traffic sign detection and recognition not only can afford the information immediately to the intelligent transportation system, but also increase the security and the reliability of automatic driving. Besides, it can assist the driver to pay much attention the safety of driving. In the detection stage, geometric characters of road traffic signs, hough transform, corner detection, and projection are used to detect the exact position of the road traffic sign in the image. Also, the properties of triangle and circle are adopted to eliminate the irrelative background region. In the recognition stage, convolution, radial basis function neural network and K-d tree are used to recognize the road signs. We use the radial basis function neural network to group the possible candidates to decrease the error rate of false recognition. The K-d trees are then constructed with separate features according to the classification results of radial basis function neural network in the previous step. The K-d tree can also be used to modify the results of classifying the road sign into the wrong group. Experimental results show that most road signs can be correctly detected and recognized by our proposed method with the accuracy of 95.5%. Moreover, the method is robust against the major difficulties of road sign detection and recognition. The proposed approach would be helpful for the development of intelligent Driver Support System and to provide effective driving assistance message.

書名頁 i
中文摘要 ii
Abstract iii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii

第一章 簡介 1
1.1 研究動機與目的 1
1.2 研究背景 1
1.3 相關文獻探討 4
1.4 流程架構圖 5
1.4.1 標誌分類模型建立 5
1.4.2 標誌偵測與辨識 6
1.5 全文架構 6
第二章 交通標誌的偵測與前置處理 7
2.1 色彩空間的轉換 8
2.1.1 HSI色彩空間 9
2.1.2 交通標誌HSI門檻值 10
2.2 雜點去除 11
2.3 標誌的幾何特性 13
2.3.1 三角形標誌 13
2.3.2 圓形標誌 17
2.3.3 相連標誌 19
2.4 投影與角點的運用及圖檔正規化 20
2.4.1 投影與角點的運用 20
2.4.2 圖形正規化－雙三次內插法（Bicubic Interpolation） 28
2.5 去除背景與保留內容 29
第三章 兩階段交通標誌分類與辨識 34
3.1 訓練之樣本取得 34
3.2 小波轉換與迴旋積 35
3.3 特徵值介紹 37
3.3.1 角度因子 37
3.3.2 徑向角度轉換（Angular Radial Transform, ART） 38
3.3.3 熵（Entropy） 39
3.3.4 K-d樹中補強之特徵值－重心與組成份子（component） 40
3.3.5 特徵值的調整 40
3.4 半徑式函數類神經網路（Radial Basis Function Neural Network） 41
3.4.1 輸入層至隱藏層 43
3.4.2 隱藏層至輸出層 46
3.5 半徑式函數類神經網路架構 48
3.5.1 訓練規則 48
3.6 K-d樹 51
第四章 實驗結果 55
4.1 交通標誌來源影像資料庫建立 56
4.2 實驗辨識之結果 58
4.2.1 半徑式函數類神經網路分群 59
4.2.2 K-d樹建構結果 60
4.2.3 紅色三角形標誌辨識之結果 68
4.2.4 紅色圓形標誌辨識之結果 69
4.2.5 藍色圓形標誌辨識之結果 71
4.3 辨識分析 72
4.3.1 ㄧ般辨識 72
4.3.2 特殊情況之辨識 74
4.3.3 辨識錯誤分析 76
4.4相關實驗比較 77
第五章 結論與未來研究方向 79
參考文獻 82

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