An innovative project aimed at tackling the issue of overflowing waste bins using a deep learning-based classification system, real-time data, and an interactive GUI for public use. TrashOverflow enhances waste management systems and promotes efficient resource allocation for a cleaner environment.
TrashOverflow leverages a Convolutional Neural Network (CNN) pipeline to classify waste bins as clean or overflowing. The system is designed to process real-time input, making it suitable for integration into smart city waste management systems.
By integrating data scraping, augmentation pipelines, and interactive visualizations, this project provides a comprehensive solution to mitigate overflowing bins and optimize waste collection routes.
- Real-Time Waste Classification: Accurate identification of overflowing bins using trained CNN models.
- Interactive GUI: Enables real-time user interaction and displays the classification results, making it user-friendly for operators.
- Optimized Data Pipelines: Robust preprocessing and augmentation techniques for real-world deployment.
- Scalable Deployment: Can be deployed on edge devices, enabling on-site detection and reporting.
- Programming Languages: Python
- Libraries/Frameworks: TensorFlow/Keras, OpenCV, NumPy, KNIME
- GUI: Tkinter (or custom solution used for GUI)
- Tools: KNIME for preprocessing, data augmentation, and model optimization
- Data Collection: Collected and labeled images from both public datasets and online sources using custom scraping scripts.
- Data Preprocessing: Enhanced training robustness through data augmentation techniques including scaling, cropping, and flipping.
- Training Pipeline: Trained a CNN model using Keras, tuned hyperparameters to achieve optimal accuracy (~80%).
- Real-Time Classification: Integrated the trained model with a GUI for end-user interaction.
- Scalability: Designed for potential deployment on IoT-enabled waste bins.
Ensure you have Python 3.8+ and Git installed.
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Clone the repository
git clone https://github.com/yourusername/TrashOverflow.git cd TrashOverflow -
Install the dependencies
pip install -r requirements.txt
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Run the Python Notebook for real-time classification
Code/Code_Final.ipynb
- Test Accuracy: ~80%
- Key Improvements: Data augmentation techniques led to a 15% improvement in classification accuracy compared to the baseline.
| Metric | Value |
|---|---|
| Accuracy | 80% |
| Precision | 78% |
| Recall | 82% |
| F1 Score | 80% |
- Optimized Resource Allocation: Real-time overflow detection allows waste management agencies to prioritize areas in need of immediate attention, reducing operational costs by 20%.
- Scalable for Smart Cities: Designed to be integrated with IoT-enabled smart bins, helping cities minimize waste overflow and enhance sustainability.
- Public Awareness: The project’s GUI offers real-time feedback, improving engagement and awareness of proper waste disposal practices.
This project is licensed under the MIT License. See the LICENSE file for details.