(Luận văn) design of advanced driver assistance system based on deep learning

MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION GRADUATION PROJECT COMPUTER ENGINEERING TECHNOLOGY DESIGN OF ADVANCED DRIVER ASSISTANCE SYSTEM BASED ON DEEP LEARNING LECTURER: LE MINH THANH, M.Eng STUDENT: THAI HOANG MINH TAM SKL010587 Ho Chi Minh City, December 2022 HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY FOR HIGH QUALITY TRAINING GRADUATION PROJECT DESIGN OF ADVANCED DRIVER ASSISTANCE SYSTEM BASED ON DEEP LEARNING Student: THAI HOANG MINH TAM ID: 18119040 Major: COMPUTER ENGINEERING TECHNOLOGY Advisor: LE MINH THANH, MEng Ho Chi Minh City, December 2022 HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY FOR HIGH QUALITY TRAINING GRADUATION PROJECT DESIGN OF ADVANCED DRIVER ASSISTANCE SYSTEM BASED ON DEEP LEARNING Student: THAI HOANG MINH TAM ID: 18119040 Major: COMPUTER ENGINEERING TECHNOLOGY Advisor: LE MINH THANH, MEng Ho Chi Minh City, December 2022 THE SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom– Happiness -Ho Chi Minh City, December 25, 2022 PRE-DEFENSE EVALUATION SHEET Student name: Thái Hoàng Minh Tâm Student ID: 18119040 Major: Computer Engineering Technology Class: 18119CLA1 Project title: Design of advanced driver assistance system based on deep learning Name of Reviewer: EVALUATION Content and workload of the project Strengths: Weaknesses: Approval for oral defense? (Approved or denied) Overall evaluation: (Excellent, Good, Fair, Poor) Mark: ……………… (In words: ) Ho Chi Minh City, December 25, 2022 REVIEWER (Sign with full name) THE SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom– Happiness -Ho Chi Minh City, December 25, 2022 EVALUATION SHEET OF DEFENSE COMMITTEE MEMBER Student name: Thái Hoàng Minh Tâm Student ID: 18119040 Major: Computer Engineering Technology Class: 18119CLA1 Project title: Design of advanced driver assistance system based on deep learning Name of Defense Committee Member: EVALUATION Content and workload of the project Strengths: Weaknesses: Overall evaluation: (Excellent, Good, Fair, Poor) Mark: ……………… (In words: ) Ho Chi Minh City, December 25, 2022 COMMITTEE MEMBER (Sign with full name) Acknowledgment I would like to express my deepest gratitude to my advisor, Mr Le Minh Thanh, MEng, for many interesting weekly technical discussions that helped me find research directions and advice on technical issues His patience and devotion to teaching have helped me become a better student in terms of both knowledge and personality, not only during the development of the graduation project but throughout the academic years I would like to extend my sincere thanks to the teachers of the Faculty of High Quality Training and the Faculty of Electrical and Electronics Engineering for their advanced knowledge and experience through every course I am also grateful that my colleagues created the best conditions for me to complete the graduation project Additionally, this endeavor would not have been possible without BOSCH Global Software Technologies Company, which financed my research Lastly, I would be remiss in not mentioning my family and friends Their belief and support in me have kept my spirits and motivation high during this process Disclaimer This thesis is the result of my study, evaluation, and implementation All texts, quoted directly or paraphrased, have been indicated by in-text citations Full bibliographic details are given in the reference list containing internet sources containing URLs Thái Hoàng Minh Tâm Table of Contents List of Figures i List of Tables iv Abstract v List of Abbreviations .vi CHAPTER 1: INTRODUCTION 1.1 OVERVIEW 1.2 GOALS 1.3 LIMITATIONS 1.4 OUTLINES CHAPTER 2: LITERATURE REVIEW 2.1 DEEP LEARNING 2.1.1 Convolutional Neural Network 2.1.2 Convolutional Layer 2.1.3 Pooling Layer .7 2.1.4 Fully Connected Layer .8 2.2 OBJECT DETECTION 2.2.1 Two-Stage Object Detection 2.2.2 One-Stage Object Detection 10 2.3 YOLOv6 OBJECT DETECTION ARCHITECTURE 13 2.3.1 RepVGG Backbone 14 2.3.2 RepPAN Neck 17 2.3.3 Decoupled Head .18 CHAPTER 3: SYSTEM DESIGN 20 3.1 OVERALL SYSTEM 20 3.2 COMPARISON OF OBJECT DETECTION MODELS 22 3.3 TRAFFIC SIGN RECOGNITION .25 3.3.1 TSR Overview 25 3.3.2 Training Process .27 3.3.3 TSR Algorithm 33 3.4 FORWARD COLLISION WARNING .34