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研究生:施建合
研究生(外文):SHI,JIAN-HE
論文名稱:車載攝影系統之交通標誌辨識技術開發
論文名稱(外文):In-Car Camera System-based Traffic Sign Recognition Technology Development
指導教授:林惠勇
指導教授(外文):LIN,HUEI-YUNG
口試委員:徐繼聖吳俊霖陳金聖
口試委員(外文):HSU,GEE-SERNWU,JIUNN-LINCHEN,CHIN-SHENG
口試日期:2016-07-28
學位類別:碩士
校院名稱:國立中正大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:68
中文關鍵詞:交通標誌支持向量機類神經網路方向梯度直方圖特徵灰階影像
外文關鍵詞:Traffic signSupport Vector MachinesNeural networkHistogram of oriented gradientsGray scale image
相關次數:
  • 被引用被引用:1
  • 點閱點閱:226
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
在車輛普及的現代社會,行車的安全一直是一個非常重要的課題,對於一般的駕駛而言,若能有一套完善的輔助駕駛行車系統,將能大大降低事故發生的機率。另一方面,隨著科技的進步,無人車的發展也開始受到重視,越來越多關於先進駕駛輔助系統(Advanced Driver Assistance Systems)的研究也被提出。本論文以視覺技術為基礎,提出一套道路交通標誌的自動偵測與辨識系統,隨時提醒駕駛路況資訊,增加行駛上的安全。以行車紀錄器的影像作為資料來源,對影像擷取梯度直方圖特徵(Histogram of oriented gradient),並使用支持向量機(Support Vector Machines)作為初步的偵測,接著利用Bilateral Chinese transform和Vertex and Bisector Transform擷取交通標誌資訊,最後利用類神經網路辨識出交通標誌的內容。本論文的方法著重在偵測部分,捨棄了影像中顏色的資訊,以影像的邊緣和梯度資訊為特徵,可以排除許多因為顏色干擾而造成的偵測錯誤,惟處理速度尚無法達到即時偵測,因此處理速度將是未來改善的重點。
The thesis addresses on the automatic traffic signs recognition method based on image processing, bilateral Chinese transform and vertex and bisector transform techniques. It is the critical safety issue of people driving car under unpredictable manual control car. However, the risk can be reduced by the complete auxiliary vehicle system. This thesis proposed an idea of automatic detection and identification system on traffic sign by using the computer vision techniques. We take images from the video camera equipped with the car and adopt the histogram of oriented gradient to form feature vectors. The support vector machines are then used to detect the traffic signs firstly. The bilateral Chinese transform and vertex and bisector transform are used to catch the area of traffic sign from images. Finally, the neural network is used to identify the instruction of the traffic sign is. To detect of sign area of an image, the edge image and gradient values of the image are the symbols without using the uncertain image color features. This system is a real-time image analysis and computing speed should be improved in the future work. The thesis proposed an image based solution for traffic sign recognition and the solution can be applied to a function of the automatic driving car in future.
目錄
摘要 i
Abstract ii
圖目錄 vii


1 緒論 1

1.1 研究動機與背景 1
1.2 相關文獻及研究 2
1.2.1 標誌之色彩、梯度及幾何特徵 2
1.2.2 特徵擷取與分類器 4
1.3 系統架構 6
1.4 論文架構 8


2 交通標誌偵測 9

2.1 Histograms of oriented gradients 13
2.2 支持向量機分類 17
2.2.1 SVM 的線性分割 17
2.2.2 SVM 的非線性分割 18
2.3 SVM 之訓練與偵測 21
2.3.1 訓練 21
2.3.2 偵測 22



3 擷取交通標誌內容與辨識 24
3.1 BCT 與VBT 介紹 24
3.1.1 Bilateral Chinese Transform 24
3.1.2 Vertex and Bisector Transform 31
3.2 以類神經網路做辨識 35
3.2.1 類神經網路架構 35
3.2.2 訓練類神經網路 38
3.3 辨識交通標誌流程 40


4 實驗結果 42

4.1 偵測結果 43
4.2 辨識結果 45
4.3 綜合結果分析 50
5 結論與未來展望 51
參考文獻 52
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