000			01907nam a2200193Ia 4500
008			110104s9999 xx 000 0 und d
082			_aMG TH-0228 _bDAT
100			_aDatir, Raunak (PG190733) _990584
245		0	_aAuto-detection of potholes using photogrammetric and deep learning approach (Softcopy is also available)
260			_c2021
300			_axiii,52p.
505			_aContents 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
700			_aSamal, Dipak (Guide) _990700
700			_aShahdadpuri, Suresh (Guide) _990701
890			_aIndia
891			_a2019 Batch
891			_aFT-PG
891			_aM.Sc. Geomatics
999			_c69735 _d69735