A data science senior thesis project by Omar Moustafa, Malak Elsayed, & Nour Kahky
Department of Mathematics and Actuarial Science, The American University in Cairo
Supervised by Dr. Noha Youssef · In collaboration with Nestlé and Dawar
This is an end-to-end deep learning pipeline that automatically analyzes incoming waste from a single image and classifies it across four critical dimensions:
| Task | Model Architecture | Validation Accuracy |
|---|---|---|
| Liquid Detection | MobileNetV2 | 94.7% |
| Single vs. Multi-Component Detection | MobileNetV2 | 94.5% |
| Recyclable vs. Non-Recyclable Classification | DenseNet121 | 96.8% |
| Waste Material Classification | Xception | 94.3% |
The four independently trained CNN models are integrated into a sequential decision pipeline that mirrors real-world bin behavior. On an external real-world validation dataset provided by Dawar, the unified pipeline achieved 92.4% accuracy.
This research was funded by Nestlé and Dawar, and culminated in a physical smart bin prototype built by AUC's Eltoukhy Learning Factory and MakersGate, integrating the deep learning pipeline with sensors, cameras, and embedded hardware for real-world deployment.
The pipeline processes each input image through four sequential stages, as illustrated by the following figure:
Figure 1. Sequential multi-stage SmartSort waste classification pipeline.
Decision Codes:
| Code | Meaning | Action |
|---|---|---|
| 1 | Liquid detected | Prompt user to empty liquid |
| 2 | Multi-component item | Prompt user to separate components |
| 3 | Recyclable — Plastic | Dispense to plastic compartment |
| 4 | Recyclable — Paper | Dispense to paper compartment |
| 5 | Recyclable — Organic | Dispense to organic compartment |
| 6 | Recyclable — Other | Dispense to general recycling compartment |
| 7 | Non-recyclable | Dispense to general waste compartment |
smartsort/
│
├── images/
│ ├── system_architecture.png # Pipeline architecture diagram
│ └── prototype_design.png # Physical smart bin prototype
│
├── models/ # ⚠️ Weights not included — see note below
│ ├── final_adjusted_liquid_advance.h5 # Liquid detection model (MobileNetV2)
│ ├── component_model_fixed_v3.h5 # Component detection model (MobileNetV2)
│ ├── DenseNet121_binary_v2.keras # Recyclability classifier (DenseNet121)
│ └── xception_4classes.keras # Material classifier (Xception)
│
├── notebooks/
│ └── smart_waste_bin_pipeline_may22.ipynb # Unified inference pipeline (Google Colab)
│
├── requirements.txt # Full dependency list
└── README.md
⚠️ Model weights are not included in this repository due to file size constraints. To run the pipeline, you will need the following four files:
final_adjusted_liquid_advance.h5component_model_fixed_v3.h5DenseNet121_binary_v2.kerasxception_4classes.kerasContact the authors for access.
- Python 3.9+
- TensorFlow 2.19.0
- Google Colab (recommended) or a local GPU environment
pip install tensorflow==2.19.0 opencv-python-headless numpy pillowOr install all dependencies from the full requirements file:
pip install -r requirements.txtThe main pipeline is contained in notebooks/smart_waste_bin_pipeline_may22.ipynb. It is designed to run in Google Colab.
- Upload the notebook to Google Colab.
- Upload all four model files to your Colab session (or mount Google Drive and update the model paths).
- Run all cells to load the models.
- When prompted, upload a waste image — the pipeline will run all four classification stages and return a decision code and action message.
# Model paths — update these if needed
LIQUID_MODEL_PATH = 'final_adjusted_liquid_advance.h5'
COMPONENT_MODEL_PATH = 'component_model_fixed_v3.h5'
BINARY_MODEL_PATH = 'DenseNet121_binary_v2.keras'
CATEGORY_MODEL_PATH = 'xception_4classes.keras'============================================================
PROCESSING: cola_bottle.jpeg
============================================================
🔍 Stage 1 · Liquid Detection
raw=0.8231 → ✅ No liquid (82.3%)
🔍 Stage 2 · Component Detection
raw=0.9104 → ✅ Single component (91.0%)
🔍 Stage 3 · Waste Classification
♻️ RECYCLABLE — PLASTIC (97.2%)
============================================================
🎯 DECISION CODE : 3
📢 ACTION : Dispensing to Plastic recyclable compartment
============================================================
- Architecture: MobileNetV2 (transfer learning)
- Task: Binary classification — liquid vs. non-liquid content
- Input: 224 × 224 RGB image
- Threshold: 0.5 (raw score < 0.5 → liquid detected)
- Architecture: MobileNetV2 (transfer learning)
- Task: Binary classification — single vs. multi-component item (e.g., paper cup with plastic lid)
- Input: 224 × 224 RGB image
- Threshold: 0.5 (raw score < 0.5 → multi-component detected)
- Architecture: DenseNet121 (transfer learning)
- Task: Binary classification — recyclable vs. non-recyclable
- Input: 224 × 224 RGB image, DenseNet preprocessing
- Architecture: Xception (transfer learning)
- Task: Multi-class classification — Paper, Plastic, Organic, or Other
- Input: 299 × 299 RGB image, Xception preprocessing
- Only runs if the recyclability model returns "recyclable"
No large-scale public dataset for multi-task waste classification existed at the time of this project. The team built a custom labeled dataset from scratch, covering the following waste categories:
Automobile Wastes · Battery Waste · E-Waste · Glass Waste · Light Bulbs · Metal Waste · Paper Waste · Plastic Waste · Organic Waste
These were mapped to the four output classes as follows:
| Raw Category | Pipeline Class |
|---|---|
| Paper waste | Paper |
| Plastic waste | Plastic |
| Organic waste | Organic |
| E-waste, automobile, battery, glass, light bulbs, metal | Other |
External real-world validation data was provided by Dawar, an Egyptian waste management company.
| Evaluation | Accuracy |
|---|---|
| Material Classification (validation) | 94.3% |
| Recyclability Classification (validation) | 96.8% |
| Liquid Detection (validation) | 94.7% |
| Component Detection (validation) | 94.5% |
| Unified pipeline on external real-world data | 92.4% |
Figure 2. Physical SmartSort prototype developed in collaboration with AUC's Eltoukhy Learning Factory and MakersGate.
Beyond the software pipeline, this project was extended into a real-world smart bin prototype funded by Nestlé and Dawar, and fabricated by AUC's Eltoukhy Learning Factory and MakersGate. The prototype integrates:
- Camera module for image capture
- Embedded hardware running the deep learning pipeline
- Motorized compartments driven by the decision codes (1–7)
- Sensors for bin capacity and status monitoring
This project would not have been possible without the support of:
- Dr. Noha Youssef — Thesis advisor, Department of Mathematics and Actuarial Science, AUC
- Ms. Mahira Hassan — Nestlé, for industry collaboration and funding
- Mr. Amr Fathi & Mr. Youssef Sami — Dawar, for real-world data and deployment partnership
- AUC Eltoukhy Learning Factory & MakersGate — Prototype design and fabrication
This repository is shared for academic and research purposes.