[Đồ án] Smart City with Computer Vision - TG.PALLIYAGURU P.N.A, Maleesha K.L.D.D.S, Nadeeshan
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Báo cáo cuối kỳ này trình bày một khuôn khổ thành phố thông minh tích hợp, sử dụng công nghệ thị giác máy tính và IoT để tối ưu hóa quản lý đô thị, bao gồm quản lý bãi đậu xe, quản lý chất thải, phát hiện tai nạn và nâng cao giám sát.
Description
IT4010 Research Project 4th Year, 1st Semester Final Report B.Sc. (Hons) Degree in Information Technology Sri Lanka Institute of Information Technology April 2025 DECLARATION We declare that this is our work, and this proposal does not incorporate without acknowledgement any material previously submitted for a degree or diploma in any other university or Institute of higher learning and to the best of our knowledge and belief it does not contain any material previously published or written by another person except where the acknowledgement is made in the text. Name Student ID Signature PALLIYAGURU P.N.A IT21187278 Maleesha K.L.D.D.S IT21166488 Nadeeshan I.U.N IT21166556 DORANEGODA K.S.M IT21187032 The above candidate is carrying out research for the undergraduate Dissertation under our supervision. ……………………………………. 11/04/2025 ……………………………………. Ms. Wishalya Tissera Date (Supervisor) 2 ABSTRACT In recent years, rapid urbanization and the continuous increase in population density have placed significant pressure on the infrastructure of modern cities. Traditional urban management techniques, including manual surveillance operations, static waste collection processes, and conventional parking management methods, are proving to be insufficient in addressing the dynamic challenges of smart cities. To overcome these limitations, this research project, titled "Smart City with Computer Vision", proposes an integrated smart city framework powered by computer vision and IoT technologies to optimize city management processes, enhance sustainability, and improve the overall quality of life for urban residents. This research focuses on four key components essential for smart city development: Parking Management, Waste Management, Accident Detection, and Surveillance Enhancement. The Parking Management System is designed to efficiently detect vacant parking slots, track vehicle parking behavior, and implement dynamic billing systems based on the duration of sta
Résumé IA
- Nom du document
- [Đồ án] Smart City with Computer Vision - TG.PALLIYAGURU P.N.A, Maleesha K.L.D.D.S, Nadeeshan
- École / Cours
- Sri Lanka Institute of Information Technology · Xử lý ảnh
- Auteur (dans le document)
- PALLIYAGURU P.N.A Maleesha K.L.D.D.S Nadeeshan I.U.N DORANEGODA K.S.M
- Contenu
- Báo cáo đề xuất một hệ thống quản lý đô thị thông minh tích hợp, sử dụng thị giác máy tính và IoT để tối ưu hóa quản lý bãi đỗ xe, chất thải, phát hiện tai nạn và giám sát an ninh, nhằm nâng cao hiệu quả và chất lượng cuộc sống.
- Table des matières
- DECLARATION
- ABSTRACT
- ACKNOWLEDGEMENT
- LIST OF TABLES
- LIST OF FIGURES
- LIST OF ABBREVIATIONS
- 1. INTRODUCTION
- 1.1 Background
- 1.2 Literature Survey
- 1.3 Research Gap
- 1.4 Research Problem
- 1.5 Research Objectives
- 1.5.1 Main Objectives
- 1.5.2 Sub Objectives
- 2. METHODOLOGY
- 2.1. Materials and Methods
- 2.2. Problem Statement
- 2.3. Surveillance Enhancement System (Solution Design)
- 2.3.1 Data Acquisition
- 2.3.2 Data Processing
- 2.3.3 Test plan and strategy
- 2.3.4 Implementation
- 2.4 Smart Waste Management System (Solution Design)
- 2.4.1 Data Acquisition
- 2.4.2 Data Processing
- 2.4.3 Test plan and strategy
- 2.4.4 Implementation
- 2.5 Accident Detection (Solution Design)
- 2.5.1 Data Acquisition
- 2.6 Smart Parking System (Solution Design)
- 2.6.1 Hardware Implementation
- 2.6.2 Number Plate Recognition and Data Management
- 2.6.3 Empty Parking Space Detection via Pixel-Based Analysis
- 2.6.4 Automated Gate Control
- 2.6.5 Implementation of Dynamic Pricing and Billing System
- 2.6.7 Development of Administrative Panel
- 2.6.8 System Validation and Testing
- 2.7 Commercialization Aspects of The Product
- 2.7 Testing
- 2.7.1 Test Plan and Strategy
- 2.7.7 Test Cases Design
- 3 Results and Discussions
- 3.1 Results
- 3.2 Research Findings
- 3.2.1 Model Architecture and Training
- 3.2.2 Performance Metrics
- 3.2.3 Robustness and Practical Application
- 3.2.4 Significance and Implications
- 3.3 Discussion
- 5. SUMMARY OF EACH STUDENT
- 5.1 Student 1 – P.N.A. PALLIYAGURU - IT21187278
- 5.2 Student 2 – Maleesha K.L.D.D.S - IT21166488
- 5.3 Student 3 – Nadeeshan I.U.N - IT21166556
- 5.4 Student 4 – DORANEGODA K.S.M - IT21187032
- 5.5 Final Remark on Student Contributions
- 6. CONCLUSION
- 7. REFERENCES
- 8. APPENDICES
- 7. Table 1: Dataset Count by Each Category
- Table 2: Dataset Count by Each Category
- Table 3: Verify Image Upload
- Table 4: Verify classification prediction by the model
- Table 5: Verify camera detects movement
- Table 6: Verify generates an automated alert
- Table 7: Verify stored correctly in the database
- Table 8: Verify charts, and incident reports data
- Table 9: Verify Waste Image Upload
- Table 10: Verify Waste Classification Prediction
- Table 11: Verify Automated Bin Lid Opening
- Table 12: Verify Bin Level Monitoring
- Table 13: Verify OLED Display Output
- Table 14: Verify Audio Output
- Table 15: Verify Data Storage
- Table 16: Verify Dashboard Data Visualization
- Table 17Verify Camera Video Feed Display
- Table 18: Verify Accident Detection by AI Model
- Table 19: Verify Location Identification of Detected Accident
- Table 20: Verify Automated Alert Generation for Accident
- Table 21: Verify Data Storage in Database for Detected Accidents
- Table 22: Verify Dashboard Analytics and Incident Reports for Accident Detection
- Figure 1: Regional waste generation per person
- Figure 2: Overall System Diagram
- Figure 3: Smart Surveillance Enhancement System
- Figure 4: Data Collection
- Figure 5: Smart Waste Management System
- Figure 6: Data Collection
- Figure 7: Accident Detection System
- 8. Figure 8: Data Collection
- Figure 9: Smart Parking Management System
- Figure 10: ESP32 with camera module
- Figure 11: IR Sensor
- Figure 12: Servo Motor
- Figure 13: ANPR system Numberplate Dataset
- Figure 14: Parking Space Detector Using Pixel Values
- Pages
- 75 pages
- Téléversé par
- Nguyen Le Giang
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[Đồ án] Smart City with Computer Vision - TG.PALLIYAGURU P.N.A, Maleesha K.L.D.D.S, Nadeeshan
Génération de l'aperçu...
IT4010 Research Project 4th Year, 1st Semester Final Report B.Sc. (Hons) Degree in Information Technology Sri Lanka Institute of Information Technology April 2025 DECLARATION We declare that this is our work, and this proposal does not incorporate without acknowledgement any material previously submitted for a degree or diploma in any other university or Institute of higher learning and to the best of our knowledge and belief it does not contain any material previously published or written by another person except where the acknowledgement is made in the text. Name Student ID Signature PALLIYAGURU P.N.A IT21187278 Maleesha K.L.D.D.S IT21166488 Nadeeshan I.U.N IT21166556 DORANEGODA K.S.M IT21187032 The above candidate is carrying out research for the undergraduate Dissertation under our supervision. ……………………………………. 11/04/2025 ……………………………………. Ms. Wishalya Tissera Date (Supervisor) 2 ABSTRACT In recent years, rapid urbanization and the continuous increase in population density have placed significant pressure on the infrastructure of modern cities. Traditional urban management techniques, including manual surveillance operations, static waste collection processes, and conventional parking management methods, are proving to be insufficient in addressing the dynamic challenges of smart cities. To overcome these limitations, this research project, titled "Smart City with Computer Vision", proposes an integrated smart city framework powered by computer vision and IoT technologies to optimize city management processes, enhance sustainability, and improve the overall quality of life for urban residents. This research focuses on four key components essential for smart city development: Parking Management, Waste Management, Accident Detection, and Surveillance Enhancement. The Parking Management System is designed to efficiently detect vacant parking slots, track vehicle parking behavior, and implement dynamic billing systems based on the duration of sta
Lire le document entier
- Nom du document
- [Đồ án] Smart City with Computer Vision - TG.PALLIYAGURU P.N.A, Maleesha K.L.D.D.S, Nadeeshan
- École / Cours
- Sri Lanka Institute of Information Technology · Xử lý ảnh
- Auteur (dans le document)
- PALLIYAGURU P.N.A Maleesha K.L.D.D.S Nadeeshan I.U.N DORANEGODA K.S.M
- Contenu
- Báo cáo đề xuất một hệ thống quản lý đô thị thông minh tích hợp, sử dụng thị giác máy tính và IoT để tối ưu hóa quản lý bãi đỗ xe, chất thải, phát hiện tai nạn và giám sát an ninh, nhằm nâng cao hiệu quả và chất lượng cuộc sống.
- Table des matières
- DECLARATION
- ABSTRACT
- ACKNOWLEDGEMENT
- LIST OF TABLES
- LIST OF FIGURES
- LIST OF ABBREVIATIONS
- 1. INTRODUCTION
- 1.1 Background
- 1.2 Literature Survey
- 1.3 Research Gap
- 1.4 Research Problem
- 1.5 Research Objectives
- 1.5.1 Main Objectives
- 1.5.2 Sub Objectives
- 2. METHODOLOGY
- 2.1. Materials and Methods
- 2.2. Problem Statement
- 2.3. Surveillance Enhancement System (Solution Design)
- 2.3.1 Data Acquisition
- 2.3.2 Data Processing
- 2.3.3 Test plan and strategy
- 2.3.4 Implementation
- 2.4 Smart Waste Management System (Solution Design)
- 2.4.1 Data Acquisition
- 2.4.2 Data Processing
- 2.4.3 Test plan and strategy
- 2.4.4 Implementation
- 2.5 Accident Detection (Solution Design)
- 2.5.1 Data Acquisition
- 2.6 Smart Parking System (Solution Design)
- 2.6.1 Hardware Implementation
- 2.6.2 Number Plate Recognition and Data Management
- 2.6.3 Empty Parking Space Detection via Pixel-Based Analysis
- 2.6.4 Automated Gate Control
- 2.6.5 Implementation of Dynamic Pricing and Billing System
- 2.6.7 Development of Administrative Panel
- 2.6.8 System Validation and Testing
- 2.7 Commercialization Aspects of The Product
- 2.7 Testing
- 2.7.1 Test Plan and Strategy
- 2.7.7 Test Cases Design
- 3 Results and Discussions
- 3.1 Results
- 3.2 Research Findings
- 3.2.1 Model Architecture and Training
- 3.2.2 Performance Metrics
- 3.2.3 Robustness and Practical Application
- 3.2.4 Significance and Implications
- 3.3 Discussion
- 5. SUMMARY OF EACH STUDENT
- 5.1 Student 1 – P.N.A. PALLIYAGURU - IT21187278
- 5.2 Student 2 – Maleesha K.L.D.D.S - IT21166488
- 5.3 Student 3 – Nadeeshan I.U.N - IT21166556
- 5.4 Student 4 – DORANEGODA K.S.M - IT21187032
- 5.5 Final Remark on Student Contributions
- 6. CONCLUSION
- 7. REFERENCES
- 8. APPENDICES
- 7. Table 1: Dataset Count by Each Category
- Table 2: Dataset Count by Each Category
- Table 3: Verify Image Upload
- Table 4: Verify classification prediction by the model
- Table 5: Verify camera detects movement
- Table 6: Verify generates an automated alert
- Table 7: Verify stored correctly in the database
- Table 8: Verify charts, and incident reports data
- Table 9: Verify Waste Image Upload
- Table 10: Verify Waste Classification Prediction
- Table 11: Verify Automated Bin Lid Opening
- Table 12: Verify Bin Level Monitoring
- Table 13: Verify OLED Display Output
- Table 14: Verify Audio Output
- Table 15: Verify Data Storage
- Table 16: Verify Dashboard Data Visualization
- Table 17Verify Camera Video Feed Display
- Table 18: Verify Accident Detection by AI Model
- Table 19: Verify Location Identification of Detected Accident
- Table 20: Verify Automated Alert Generation for Accident
- Table 21: Verify Data Storage in Database for Detected Accidents
- Table 22: Verify Dashboard Analytics and Incident Reports for Accident Detection
- Figure 1: Regional waste generation per person
- Figure 2: Overall System Diagram
- Figure 3: Smart Surveillance Enhancement System
- Figure 4: Data Collection
- Figure 5: Smart Waste Management System
- Figure 6: Data Collection
- Figure 7: Accident Detection System
- 8. Figure 8: Data Collection
- Figure 9: Smart Parking Management System
- Figure 10: ESP32 with camera module
- Figure 11: IR Sensor
- Figure 12: Servo Motor
- Figure 13: ANPR system Numberplate Dataset
- Figure 14: Parking Space Detector Using Pixel Values
- Pages
- 75 pages
- Téléversé par
- Nguyen Le Giang
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