Solid Waste Image Detection Using Deep Convolutional Neural Network

Project Code :TCMAPY1591

Objective

To develop a deep learning-based system capable of detecting and classifying solid waste images in real-To train and evaluate YOLOv8, YOLOv9, and YOLOv10 models on a custom dataset with six waste categories: cardboard, glass, metal, paper, plastic, and trash.

Abstract

Effective waste segregation is a critical step towards sustainable environmental management. This project, Solid Waste Image Detection Using Deep Convolutional Neural Network, aims to automate the classification of solid waste materials using advanced object detection models. Leveraging state-of-the-art YOLO (You Only Look Once) architectures—YOLOv8, YOLOv9, and YOLOv10—the system is trained to detect and classify waste into six distinct categories: cardboard, glass, metal, paper, plastic, and trash. The proposed approach enhances real-time detection capabilities through deep learning and image processing, enabling high-speed and accurate identification of waste materials. This automation not only streamlines the waste management process but also promotes recycling efforts by minimizing human error in sorting. The project is implemented using Python for the backend and Streamlit for the frontend, providing an intuitive interface for users to upload waste images and receive categorized results. Overall, the model contributes to efficient smart waste management systems, paving the way for cleaner and greener urban ecosystems.

Keywords: Solid Waste Detection, YOLOv8, YOLOv9, YOLOv10, Deep Learning, Image Classification, Waste Segregation, Object Detection, Smart Waste Management

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

4.2 Hardware Requirements

Processor: Intel i3 or equivalent for handling basic video processing.
RAM: 8 GB for smooth operation.
Storage: 160 GB hard disk.
Cameras:

High-definition CCTV cameras (1080p or higher resolution).
Frame rate: Minimum 30 frames per second (FPS).

GPU: None specified, but NVIDIA GTX 1080 or higher is recommended for faster inference in deep learning tasks.
Network: High-speed internet connection (minimum 1 Gbps) for smooth data transfer.

4.3 Software Requirements

Operating System:

Windows 7/8/10 for the front-end development.
Linux-based OS (Ubuntu 18.04 or higher) for backend deployment.

Server-side Script: HTML, CSS, Bootstrap, and JS.
Programming Languages:

Python (for backend and model training).
JavaScript, HTML, CSS (for front-end development via Streamlit).

Deep Learning Frameworks:

TensorFlow or PyTorch (for YOLOv8, v11, model implementation).
OpenCV (for video processing and frame extraction).

Libraries and Tools:

Streamlit (for the front-end interface).
Flask (for API integration between backend and front-end).
NumPy, Pandas (for data processing and analysis).

Database: MySQL or PostgreSQL (for storing logs and metadata of violations).
Version Control: Git for version control and collaboration; GitHub or GitLab for repository hosting and CI/CD pipelines.
IDE/Workbench: PyCharm or VSCode for Python development.
Server Deployment: XAMPP Server.

Demo Video

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