Automated detection and recognition of highway road marking with computer vision and deep learning (Softcopy is also available)
khan, Mehreen (PG180535)
Automated detection and recognition of highway road marking with computer vision and deep learning (Softcopy is also available) - 2020 - ix,44p.
Table of contents
Certificate of Original Authorship i
Certificate of Dissertation - Advisor’s consent iii
Certificate of Dissertation - Program Chair v
Acknowledgments vii
Abstract ix
List of figures xiii
List of tables xvii
1 Chapter I: Introduction 1
1.1 Background1
1.2 Limitations 2
1.3 Aims and Objectives2
1.4 Deliverables 2
1.5 Thesis Outline 3
2 Chapter II: Related Work4
2.1 Image Transformations4
2.1.1 OpenCV Library5
2.1.2 Pre-processing and Post-processing 7
2.1.3 Noise Reduction and Signal to Noise Ratio (SNR) 8
2.2 Road Marking 8
2.2.1 Types of Road Marking 9
2.3 Road Lane Detection 10
2.3.1 Types of Lanes 11
2.4 Advanced Driving Assistance System (ADAS)12
2.5 Convolutional Neural Networks 13
2.5.1 A Classic CNN: 13
3 Chapter III: Working Methodology16
3.1 Research Methodology16
3.1.1 System Architecture 16
3.1.2 Data Collection 17
3.2 Image Processing 17
3.2.1 Region of Interest (ROI)17
3.2.2 Thresholding 18
3.2.3 Canny Edge Detection Method19
3.2.4 Hough Line Transform Method 21
3.3 Road Lane Detection23
3.3.1 Camera Calibration 23
3.3.2 Perspective Transformation 24
3.3.3 Color Segmentation and Thresholding26
3.4 Lane Curve Fitting 27
3.4.1 Lane Curvature28
3.4.2 Sliding Window Method 28
3.5 Recognition29
3.5.1 Feature Recognition Methods29
3.6 Tracking 30
3.6.1 VSLAM 30
3.7 Vanishing Point 31
3.8 RANSAC31
3.9 Evaluation32
4 Chapter IV: Experimental Results 33
4.1 Model I 33
4.1.1 Working 33
4.2 Model II35
4.2.1 Working 35
5 Chapter V: Conclusion 37
5.1 Way Forward37
References xli
Appendix 1: XYZ xliii
Appendix 2: XYZ45
Plagiarism Report Copy46
MG TH-0195 / KHA
Automated detection and recognition of highway road marking with computer vision and deep learning (Softcopy is also available) - 2020 - ix,44p.
Table of contents
Certificate of Original Authorship i
Certificate of Dissertation - Advisor’s consent iii
Certificate of Dissertation - Program Chair v
Acknowledgments vii
Abstract ix
List of figures xiii
List of tables xvii
1 Chapter I: Introduction 1
1.1 Background1
1.2 Limitations 2
1.3 Aims and Objectives2
1.4 Deliverables 2
1.5 Thesis Outline 3
2 Chapter II: Related Work4
2.1 Image Transformations4
2.1.1 OpenCV Library5
2.1.2 Pre-processing and Post-processing 7
2.1.3 Noise Reduction and Signal to Noise Ratio (SNR) 8
2.2 Road Marking 8
2.2.1 Types of Road Marking 9
2.3 Road Lane Detection 10
2.3.1 Types of Lanes 11
2.4 Advanced Driving Assistance System (ADAS)12
2.5 Convolutional Neural Networks 13
2.5.1 A Classic CNN: 13
3 Chapter III: Working Methodology16
3.1 Research Methodology16
3.1.1 System Architecture 16
3.1.2 Data Collection 17
3.2 Image Processing 17
3.2.1 Region of Interest (ROI)17
3.2.2 Thresholding 18
3.2.3 Canny Edge Detection Method19
3.2.4 Hough Line Transform Method 21
3.3 Road Lane Detection23
3.3.1 Camera Calibration 23
3.3.2 Perspective Transformation 24
3.3.3 Color Segmentation and Thresholding26
3.4 Lane Curve Fitting 27
3.4.1 Lane Curvature28
3.4.2 Sliding Window Method 28
3.5 Recognition29
3.5.1 Feature Recognition Methods29
3.6 Tracking 30
3.6.1 VSLAM 30
3.7 Vanishing Point 31
3.8 RANSAC31
3.9 Evaluation32
4 Chapter IV: Experimental Results 33
4.1 Model I 33
4.1.1 Working 33
4.2 Model II35
4.2.1 Working 35
5 Chapter V: Conclusion 37
5.1 Way Forward37
References xli
Appendix 1: XYZ xliii
Appendix 2: XYZ45
Plagiarism Report Copy46
MG TH-0195 / KHA