TY  - BOOK
AU  - Datir, Raunak (PG190733)
AU  - Samal, Dipak  (Guide)  
AU  - Shahdadpuri, Suresh  (Guide)    
TI  - Auto-detection of potholes using photogrammetric and deep learning approach (Softcopy is also available)  
U1  - MG TH-0228 
PY  - 2021///
N1  - Contents
Certificate of original authorship	i
Certificate of Dissertation Advisor’s consent	iii
Acknowledgments	v
Abstract	vii
List of figures	ix
List of tables	xii
1	Introduction	1
1.1	Aim and Objective	1
1.2	Thesis motivation	1
1.3	Significance of the study	2
1.4	Limitation	2
1.5	Thesis outline	3
2	Literature Review	5
2.1	Road surface Distress	5
2.1.1	Crack	5
2.1.2	Raveling	5
2.1.3	Roughness	6
2.1.4	Rutting	6
2.1.5	Potholes	7
2.2	Road Distress Detection Techniques	7
2.2.1	Public Reporting	8
2.2.2	Vibration based techniques	8
2.2.3	2D and 3D vision based technique	8
2.2.4	Learning based technique	10
2.3	Inferences	12
3	Methodology	13
4	Data Collection and Processing	15
4.1	Camera Module	15
4.2	Testing	17
4.2.1	Testing without Ground Control Points (GCPs)	17
4.2.2	Testing with Ground Control Points (GCPs)	19
4.3	Study area and dataset	23
4.3.1	Open source dataset	25
5	Results and Conclusion	27
5.1	Photogrammetric approach	27
5.1.1	Steps for data processing	27
5.2	Deep learning approach	30
5.2.1	Mask RCNN	31
5.2.2	YOLO (You only look once)	35
5.3	Conclusion	38
5.4	Limitation and Drawbacks	39
5.5	Future Scope	39
References	xli
Appendix 1: Various test outputs to improve accuracy of camera module	xlv
Appendix 2: Python code	xlvii
Plagiarism Report copy	li



ER  -