HydroVexel — AI Oil Spill Detection
A deep learning system that detects oil spills from satellite imagery with 94.57% accuracy using U-Net with Attention Gates, deployed on Streamlit.
Overview
HydroVexel is an AI-powered oil spill detection system that analyzes satellite imagery and instantly identifies oil spills in the ocean — at 94.57% accuracy. Built for the GDG Noida Build-a-thon, it's aimed at protecting marine environments through rapid detection and faster response.
Why It Matters
Oil spills devastate marine life, pollute coastlines, damage fishing and tourism economies, and cause long-term ecological harm. Traditional detection is slow and manual — HydroVexel makes it instant and accurate, so response teams can act faster.
What I Built
- Real-time detection — upload a satellite image, get an instant result
- 94.57% accuracy from a U-Net segmentation model with Attention Gates
- Visual analysis — heatmaps, segmentation overlays, and confidence scores
- Cloud storage to save and track detections over time
- Zero-install web app with an ocean-themed, animated UI
How It Works
A modular segmentation pipeline:
- Preprocessing — image resizing, normalization, and augmentation
- Model — U-Net with Attention Gates focuses the network on spill regions
- Inference — pixel-level segmentation produces the spill mask
- Visualization — overlays, heatmaps, and a confidence score are rendered back to the user
Tech Stack
ML / CV: Python, TensorFlow, U-Net + Attention Gates, OpenCV
App & Deploy: Streamlit (live), cloud storage for detection history
Use Case
Environmental agencies and maritime responders who need rapid, automated screening of satellite imagery to spot oil spills early — replacing slow manual review with instant, explainable detection.
Outcome
Built for the GDG Noida Build-a-thon; deployed and publicly usable on Streamlit with a measured 94.57% detection accuracy.