https://github.com/zaabola/projet_mining_pollution
EcoGuard AI: A comprehensive computer vision platform for environmental monitoring and worker safety. Features real-time detection models for PPE compliance, mining footprint segmentation, deforestation tracking, soil health analysis, and wildlife monitoring via an interactive Django dashboard.
https://github.com/zaabola/projet_mining_pollution
ai computer-vision django llm minning pandas python pytorch web webdevelopment
Last synced: about 2 months ago
JSON representation
EcoGuard AI: A comprehensive computer vision platform for environmental monitoring and worker safety. Features real-time detection models for PPE compliance, mining footprint segmentation, deforestation tracking, soil health analysis, and wildlife monitoring via an interactive Django dashboard.
- Host: GitHub
- URL: https://github.com/zaabola/projet_mining_pollution
- Owner: zaabola
- Created: 2026-05-02T14:31:51.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2026-05-02T15:16:04.000Z (about 2 months ago)
- Last Synced: 2026-05-02T16:27:27.491Z (about 2 months ago)
- Topics: ai, computer-vision, django, llm, minning, pandas, python, pytorch, web, webdevelopment
- Language: HTML
- Homepage:
- Size: 5.66 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# EcoGuard AI 🌍🛡️
**EcoGuard AI** is a comprehensive, AI-powered computer vision platform designed to tackle industrial pollution, enforce worker safety compliance, and monitor environmental health.
Developed at **ESPRIT (École Supérieure Privée d'Ingénierie et de Technologies / Esprit School of Engineering)**, EcoGuard utilizes advanced deep learning architectures (YOLOv8 & PyTorch U-Net) and is deployed via a modern Django web application. It provides real-time, interactive insights into high-risk industrial zones (like mining operations) and their surrounding ecosystems.
---
## ✨ Key Features
### 👷♂️ Industrial Worker Safety
* **PPE Compliance Tracking:** Real-time YOLOv8 models fine-tuned to detect hard hats, medical masks, and heavy-duty gas masks.
* **Explainable AI (XAI):** EigenCAM integrations to visualize exactly *what* the model is looking at when making safety predictions.
### 🏭 Environmental & Mining Monitoring
* **Mining Area Segmentation:** PyTorch U-Net models trained on satellite imagery to precisely calculate the footprint of legal and illegal mining operations.
* **Soil Health Analysis:** The custom "Ghada" U-Net model evaluates land degradation and soil health based on the proximity and density of mining excavations.
* **Deforestation Tracking:** Automated tracking of forest loss around industrial zones using custom satellite segmentation.
* **Smoke & Fire Detection:** Early warning systems for industrial exhaust and wildfires.
### 🐟 Wildlife & Ecosystem Tracking
* **Aquatic Contamination Analysis:** Advanced fish behavior tracking. The system uses Re-Identification (Re-ID) and trajectory mapping to classify fish swimming patterns (Normal vs. Stressed) as an early indicator of phosphogypsum water contamination.
* **Terrestrial Wildlife:** YOLO-based animal detection to monitor wildlife displacement near mining zones.
### 🔐 Smart Authentication & Administration
* **OCR-Powered Registration:** Employees register by uploading their ID cards. The system uses `EasyOCR` to automatically extract their First and Last names, auto-generate a corporate email (`first_last@EcoGuard.ai`), and generate a highly secure random password.
* **Approval Workflow:** New accounts are placed in a "Pending Approval" state.
* **Admin Dashboard:** Superusers have access to a User Management interface to view ID cards and securely Approve, Reject, or Delete employee access.
---
## 🛠️ Technology Stack
**Machine Learning & Computer Vision:**
* `PyTorch` / `Torchvision`
* `Ultralytics YOLOv8`
* `Segmentation Models PyTorch (SMP)`
* `OpenCV` / `NumPy`
* `EasyOCR`
**Web Development:**
* `Django` (Python Web Framework)
* `SQLite3` (Database)
* `Bootstrap 5` (Mazer Admin Template)
* `Chart.js` / `ApexCharts` (Data Visualization)
---
## 🚀 Installation & Setup
1. **Clone the repository:**
```bash
git clone https://github.com/zaabola/Projet_Mining_Pollution.git
cd Projet_Mining_Pollution
```
2. **Create and activate a virtual environment:**
```bash
python -m venv venv
# On Windows:
venv\Scripts\activate
# On Mac/Linux:
source venv/bin/activate
```
3. **Install dependencies:**
*(Ensure you have PyTorch installed according to your CUDA version first)*
```bash
pip install -r requirements.txt
```
4. **Run Database Migrations:**
```bash
cd web_app
python manage.py makemigrations
python manage.py migrate
```
5. **Create an Admin User:**
```bash
python manage.py createsuperuser
```
6. **Start the Development Server:**
```bash
python manage.py runserver
```
Access the dashboard at `http://localhost:8000`.
---
## 📸 Screenshots
---
## 📄 Credits & Academic Context
This project was developed at **ESPRIT (Esprit School of Engineering)** as part of an advanced environmental technology and artificial intelligence initiative. All rights reserved.