[Đồ án] Smart Waste Management System - TG.Maleesha K.L.D.D.S
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Báo cáo cuối kỳ về một hệ thống quản lý chất thải thông minh sử dụng IoT, thị giác máy tính và học máy để cải thiện hiệu quả thu gom, phân loại và giám sát mức độ đầy của thùng rác, đồng thời giảm chi phí và nâng cao vệ sinh môi trường đô thị.
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- Nom du document
- [Đồ án] Smart Waste Management System - TG.Maleesha K.L.D.D.S
- Auteur (dans le document)
- Maleesha K.L.D.D.S
- Contenu
- Báo cáo giới thiệu một Hệ thống Quản lý Chất thải Thông minh ứng dụng IoT, thị giác máy tính và học máy để cải thiện hiệu quả thu gom, phân loại rác và giám sát mức độ đầy của thùng rác. Hệ thống này hứa hẹn mang lại lợi ích về chi phí, môi trường và sức khỏe cộng đồng.
- Table des matières
- Table of Contents 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
- 2.1.1. Problem Statement
- 2.1.2. Component System Architecture (Solution Design)
- 2.1.3. Data Acquisition
- 2.1.4. Processing and Augmentation
- 2.2. Commercialization Aspects of The Product
- 2.3. Implementation
- 2.4. Testing
- 2.4.1. Test plan and strategy
- 2.4.2. Test Case Design
- 3. Results and Discussions
- 3.1. Results
- 3.2. Product Development
- 3.3. Research Findings
- 3.3.1. Model Architecture and Training
- 3.3.2. Performance Metrics
- 3.3.3. Robustness and Practical Application
- 3.3.4. Significance and Implications
- 3.4. Discussion
- 4. CONCLUSION
- 5. REFERENCES
- 6. APPENDICES
- 6. Table 1: Characteristics of Existing Research Literature and the Proposed System
- Table 2: Dataset Count by Each Category
- Table 3: Verify Waste Image Upload
- Table 4: Verify Waste Classification Prediction
- Table 5: Verify Automated Bin Lid Opening
- Table 6: Verify Bin Level Monitoring
- Table 7: Verify OLED Display Output
- Table 8: Verify Audio Output
- Table 9: Verify Data Storage
- Table 10: Verify Dashboard Data Visualization
- Figure 1: Projected waste generation, by region (millions of tons/year)
- Figure 2: Regional waste generation per person
- Figure 3: Proposed System for Smart Waste Management
- Figure 4: Arduino UNO Board
- Figure 5: LGS-01 Servo Motor
- Figure 6: Web Camera
- Figure 7: Overall System Diagram
- Figure 8: Smart Waste Management System
- Figure 9: Data Collection
- Figure 10: Hybrid Model Accuracy and Loss
- Figure 11: Analytics Dashboard Page
- Figure 12: Bin Analytics Page
- Pages
- 48 pages
- Téléversé par
- Nguyen Le Giang
Génération de l'aperçu...
Description
IT4010 Research Project 4th Year, 1st Semester Final Report IT21166488 Maleesha K.L.D.D.S 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 Maleesha K.L.D.D.S IT21166488 The above candidate is carrying out research for the undergraduate Dissertation under our supervision. ……………………………………. 10/04/2025 ……………………………………. Ms. Wishalya Tissera Date (Supervisor) 2 ABSTRACT Traditional waste management systems are increasingly becoming incompetent, more so with rapidly growing urban populations, leading to challenges of overflowing dumpsites, compromised routes, inefficiency, and heightened environmental concerns. There is the development of a Smart Waste Management System that approaches the issue through the strength of the IOT, computer vision, and machine learning to design an intelligent, responsive, and efficient waste management system. Optimally, how waste collection processes could be managed would result in cost savings and a cleaner urban environment. Waste type classifiers at the core of the system use machine learning algorithms to identify different types of waste and assist in automatic sorting to improve recycling processing. The waste bins will be endowed with computer vision capabilities through a design of the garbage bin recognition model to cut down the activities that go on in waste collection for the ease of disposing of waste from the waste bin into the collection unit. System-wise, an interactive mechanism for the user and an audio feedback voice will be applie
[Đồ án] Smart Waste Management System - TG.Maleesha K.L.D.D.S
Génération de l'aperçu...
IT4010 Research Project 4th Year, 1st Semester Final Report IT21166488 Maleesha K.L.D.D.S 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 Maleesha K.L.D.D.S IT21166488 The above candidate is carrying out research for the undergraduate Dissertation under our supervision. ……………………………………. 10/04/2025 ……………………………………. Ms. Wishalya Tissera Date (Supervisor) 2 ABSTRACT Traditional waste management systems are increasingly becoming incompetent, more so with rapidly growing urban populations, leading to challenges of overflowing dumpsites, compromised routes, inefficiency, and heightened environmental concerns. There is the development of a Smart Waste Management System that approaches the issue through the strength of the IOT, computer vision, and machine learning to design an intelligent, responsive, and efficient waste management system. Optimally, how waste collection processes could be managed would result in cost savings and a cleaner urban environment. Waste type classifiers at the core of the system use machine learning algorithms to identify different types of waste and assist in automatic sorting to improve recycling processing. The waste bins will be endowed with computer vision capabilities through a design of the garbage bin recognition model to cut down the activities that go on in waste collection for the ease of disposing of waste from the waste bin into the collection unit. System-wise, an interactive mechanism for the user and an audio feedback voice will be applie
Lire le document entier
- Nom du document
- [Đồ án] Smart Waste Management System - TG.Maleesha K.L.D.D.S
- Auteur (dans le document)
- Maleesha K.L.D.D.S
- Contenu
- Báo cáo giới thiệu một Hệ thống Quản lý Chất thải Thông minh ứng dụng IoT, thị giác máy tính và học máy để cải thiện hiệu quả thu gom, phân loại rác và giám sát mức độ đầy của thùng rác. Hệ thống này hứa hẹn mang lại lợi ích về chi phí, môi trường và sức khỏe cộng đồng.
- Table des matières
- Table of Contents 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
- 2.1.1. Problem Statement
- 2.1.2. Component System Architecture (Solution Design)
- 2.1.3. Data Acquisition
- 2.1.4. Processing and Augmentation
- 2.2. Commercialization Aspects of The Product
- 2.3. Implementation
- 2.4. Testing
- 2.4.1. Test plan and strategy
- 2.4.2. Test Case Design
- 3. Results and Discussions
- 3.1. Results
- 3.2. Product Development
- 3.3. Research Findings
- 3.3.1. Model Architecture and Training
- 3.3.2. Performance Metrics
- 3.3.3. Robustness and Practical Application
- 3.3.4. Significance and Implications
- 3.4. Discussion
- 4. CONCLUSION
- 5. REFERENCES
- 6. APPENDICES
- 6. Table 1: Characteristics of Existing Research Literature and the Proposed System
- Table 2: Dataset Count by Each Category
- Table 3: Verify Waste Image Upload
- Table 4: Verify Waste Classification Prediction
- Table 5: Verify Automated Bin Lid Opening
- Table 6: Verify Bin Level Monitoring
- Table 7: Verify OLED Display Output
- Table 8: Verify Audio Output
- Table 9: Verify Data Storage
- Table 10: Verify Dashboard Data Visualization
- Figure 1: Projected waste generation, by region (millions of tons/year)
- Figure 2: Regional waste generation per person
- Figure 3: Proposed System for Smart Waste Management
- Figure 4: Arduino UNO Board
- Figure 5: LGS-01 Servo Motor
- Figure 6: Web Camera
- Figure 7: Overall System Diagram
- Figure 8: Smart Waste Management System
- Figure 9: Data Collection
- Figure 10: Hybrid Model Accuracy and Loss
- Figure 11: Analytics Dashboard Page
- Figure 12: Bin Analytics Page
- Pages
- 48 pages
- Téléversé par
- Nguyen Le Giang
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Aucun commentaire pour le moment. Soyez le premier !
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