🛣️ Pothole Preventer – Smart Path Planning for Accident Prevention

📌 Overview

Pothole Preventer is a project that detects road damage in real time and suggests the smoothest and safest path for a vehicle to follow.
It uses YOLOv8 segmentation to detect potholes and road damage, overlays a heatmap of road conditions, and computes an optimal driving path by minimizing cumulative damage along the route.

The system works with both pre-recorded videos and live camera feeds.

🚀 Features

• ✅ YOLOv8 segmentation for accurate pothole/damage detection
• ✅ Real-time heatmap generation showing road quality
• ✅ Dynamic path planning (minimizes total damage)
• ✅ Smoothed path rendering (no jerky swerving, suitable for real cars)

⚙️ Instructions for Local Execution

1. Clone the Repository

git clone https://github.com/CodeThread386/Pothole-Preventer.git
cd Pothole-Preventer

2. Install Dependencies

Install the required Python packages:

pip install ultralytics opencv-python numpy matplotlib

3. Begin Execution

python app.py

▶️ Usage

Open app.py and make modifications in line 11

Run on a Video File

VIDEO_SOURCE = "your video file path"

Run on Live Camera

VIDEO_SOURCE = 0

📊 How It Works

1. Segmentation – YOLOv8 detects potholes/damage and masks them.
2. Tile Grid Overlay – The road is divided into tiles, each scored by damage percentage.
3. Path Planning – At each frame, the algorithm chooses the next tile to minimize cumulative damage.
4. Path Smoothing – The path is filtered to remove jerky swerves, producing a realistic drivable line.
5. Visualization – Heatmap and the recommended driving path (yellow) are overlaid on the video.

🔮 Future Improvements

• 📍 GPS integration for real-world navigation
• 🚗 Lane aware planning
• 🧠 Adaptive smoothing based on vehicle dynamics
• 📱 Android auto and Apple car app deployment with dashcam integration