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研究生:王鈺達
研究生(外文):Yu-Da Wang
論文名稱:倒傳遞神經網路與地平面立體視覺作用於自動車導航之障礙物偵測與道路分類
論文名稱(外文):Obstacle Detection and Road Classification Applied to Navigation of Automatic Land Vehicle Based on Back-propagation Neural Network and Ground Plane Stereo Techniques
指導教授:駱榮欽駱榮欽引用關係陳文輝陳文輝引用關係
口試委員:王振興林啟芳
口試日期:2012-07-27
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:自動化科技研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:44
中文關鍵詞:自動車視差圖區塊匹配地平面對準倒傳遞神經網路
外文關鍵詞:Automatic land vehicleDisparity mapBlock matchingGround plane alignmentBack-propagation neural network
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在自動車導航領域中利用影像做環境場景的識別是十分重要的。本篇論文中利用對地平面校準後之左右影像,使校準後影像相減後地面視差為零,再由校準後影像做區塊匹配對應,取得視差圖,圖中我們可以清楚地分離地面與立於地面障礙物,並推算出障礙物深度與高度資訊,另外為使環境資訊更加充足,我們將從原始影像中取得各類型道路的顏色、紋理等特徵,再經由倒傳遞神經網路進行訓練和分類,分類的平均辨識率達85%以上,提供自動車找到可行的路面區域並做行駛的選擇,由於分類結果只是區塊性質相似,藉由3D定位資訊來驗證影像分類是否誤判,並利用高度、深度及多類道路資訊實現自動車導航系統。此研究透過PIC控制自動車平台,並由電腦端下達指令。車子控制系統分兩部分,一個由伺服馬達控制轉向部分,另一個則由直流馬達控制速度。當程式運行時,速度可達每秒15張,透過實驗可讓自動車在校園行走,證明所提方法的可行性。

In this thesis, we present an approach for environment recognition in automatic land vehicle (ALV). By using block matching for the two ground plane alignment images, we can obtain the disparity map. In the map, we can clearly detect obstacles from ground plane, and calculate the height and depth information of the obstacles. To sufficient environment information, we can obtain the features of the road (like colors, textures, etc.) from the original image. By the environment information obtained, and using back-propagation neural network (BPNN) to train and classification, the ALV will be able to recognize drivable surface, with a recognition rate of 85%, providing various options for navigation. Due to classification results are only template similar, using 3D information and road features to verify classified image will be able to achieve ALV navigation system. In this study, ALV platform is controlled by a PIC controller, and the controlling instruction is sent from a computer. The motor control system is divided into two parts: one for controlling the direction derived by a servo motor, and the other for controlling the speed of going forward or backward with a DC motor. The proposed ALV system can reach at a rate of 15 frames per second. The ALV system has been performed in NTUT campus to demonstrate the effectiveness of the proposed method.

摘 要 i
ABSTRACT ii
誌 謝 iv
TABLE OF CONTENTS v
LIST OF TABLES vii
LIST OF FIGURES viii
Chapter 1 INTRODUCTION 1
1.1 Research Motivation 1
1.2 Survey of Related Research 1
1.2.1 Stereo Vision 1
1.2.2 Road Detection 2
1.2.3 Automatic Land Vehicle Guidance Methods 2
1.3 Overview of Proposed Approaches 3
1.4 Thesis Organization 4
Chapter 2 STEREO VISION 5
2.1 Traditional Stereo Vision Flow Cart 6
2.2 Camera Model 6
2.3 Disparity Map 8
2.4 Ground Plane Alignment 9
2.5 Stereo Matching 10
Chapter 3 ROAD CLASSFICATION 13
3.1 Segmentation Using Morphological Watershed 14
3.2 Feature Extraction 16
3.2.1 Color Descriptors 16
3.2.2 Texture Descriptors 19
3.3 Feature Selection 20
3.4 Back-propagation neural network 21
3.4.1 The Principle of Neural Network 21
3.4.2 Network Setting 24
3.5 Results of Road Classification 25
Chapter 4 NAVIGATION STRATEGY 28
Chapter 5 EXPERIMENTAL RESULT 31
5.1 System Architecture 31
5.2 Ground Plane Based Disparity Map 33
5.2.1 Depth Measurement 34
5.2.2 Height Measurement 36
5.2.3 Result of Ground Plane 38
5.3 Combination of Road Classification 39
Chapter 6 CONCLUSION AND FURTHER RESEARCH 41
6.1 Conclusion 41
6.2 Further Research 41
REFERENCE 43


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[2] R. A. Hamzah, A. M. A. Hamid and S. I. M. Salim, “The Solution of Stereo Correspondence Problem Using Block Matching Algorithm in Stereo Vision Mobile Robot,” Proc. of IEEE 2nd Int. Conf. on Computer Research and Development pp. 733-737, 2010.
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[4] J. Y. He1, S. Y. Su, T. X. Chen and R. C. Lo “Outdoor Piloting of ALV Base on Stereo Vision Using Disparity Rectification,” National Defence University Conference on National Defense Science and Technology, 2009
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[7] Jia Liu, Zheying Li, Huan Zhang, Caixia Lv, “A Vision-Based Road Recognition Algorithm,” in 3rd IEEE Conference on Industrial Electronics and Applications, pp. 284-287, 2008.
[8] Hui Kong, Jean-Yves Audibert, and Jean Ponce, “General Road Detection From a Single Image,” in IEEE Transactions on Image Processing, pp. 2211-2220, 2010.
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[10] Yaniv Alon, Andras Ferencz and Amnon Shashua, “Off-road Path Following using Region Classification and Geometric Projection Constraints,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 689-696, 2006.
[11] Szu Yu Shen and Rong Chin Lo, “An Improved Multi-Classifier road Detection Applied to Navigation of Automatic Land Vehicle through Remote Calling,” Advanced Materials Research, pp.1233-1239,November, 2011
[12] 盧振育,基於立體電腦視覺及人工智慧策略搭配雷射光作室外自動車導航之研究,碩士論文,國立臺北科技大學自動化科技研究所,台北,2004.
[13] 樊曉軍,羅熊等,複雜環境下基於蟻群優化算法的機器人路徑規劃,2003,25(5):414~418
[14] Tian-Xiang Chen, Wei-Cheng Tu, and Rong-Chin, Lo, “A Study on Outdoor Guidance of Autonomous Land Vehicles Using Disparity Map Coordinated with Multi-Classifiers Based on BPNN,” The 21th CVGIP, session D-4, no. 124, Taiwan, 2008.
[15] Gary Bradski and Adrian Kaebler “Learning OpenCV”
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[19] 王進德,類神經網路與模糊控制理論入門與應用,2008


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