Smart Road Patcher

Inspiration

Every day, thousands of vehicles face damage and delays due to cracks and potholes on roads. Manual repair work is slow, costly, and unsafe. We wanted to build a smart, semi-automated machine that can detect road cracks and patch them on the spot — improving safety, reducing maintenance costs, and saving time.
This idea aligns perfectly with the Visual Difference Engine problem statement, which focuses on detecting and signaling visual changes over time.

What it does

The Smart Road Patcher is an semi-automated road maintenance system that uses a visual difference engine to detect cracks or potholes in real time. Once detected, it activates a mechanical patching unit that fills and seals the damage immediately.

• Detects cracks through camera or LiDAR-based scanning.
  
• Processes data to find surface irregularities.
  
• Patches cracks and potholes -automatically with minimal human intervention.
  
• Works day and night, improving road quality and safety.

How we built it

• Designed a mechanical base structure to hold the patching unit.
  
• Integrated camera and LiDAR sensors to scan the road surface.
  
• Used microcontrollers to process input and control actuators.
  
• Implemented a visual difference algorithm to detect surface changes.
• Designed a patching mechanism to release and apply material precisely. This makes the system compact, affordable, and suitable for both rural and urban roads.

Challenges we ran into

• Accurate crack detection with low-cost sensors.
  
• Achieving real-time processing while the machine is in motion.
• Aligning the patching system precisely with detected cracks.
  
• Power and mobility management during operation. These challenges helped us to learn about vision systems, sensor integration, and mechanical control.

Accomplishments that we're proud of

• Successfully designed and conceptualized a smart semi-automated road patching system.
  
• Developed a functional prototype for integrated with sensors and automation controls.
  
• Integrated vision-based detection with a working patching unit.
  
• Used modern technologies like Raspberry Pi, OpenCV, and Firebase for a clean, smart architecture.
  
• Created a scalable and practical solution that can help both rural and urban infrastructure.
  
• Gained strong technical and presentation experience through the hackathon.

What we learned

• How to combine mechanical engineering, IoT, and computer vision in one project.
  
• Using OpenCV and sensors to detect cracks in real time.
  
• Basics of integrating hardware and software for an automated system..
  
• How to structure a hackathon project with clear goals and scope.
  
• Working with Firebase Realtime Database for live data tracking.
  
• Understanding challenges of real-time processing in a moving system.
  
• Improved teamwork, project planning, and documentation skills.

What's next for Smart Road Patcher

• Enhancing accuracy using AI/ML algorithms.
• Integrating GPS mapping and reporting for maintenance tracking.
  
• Scaling the machine for highways and rural roads.
  
• Collaborating with civic bodies and industries for deployment.

Built With

• esp32
• firebase
• mongodb
• mqtt
• node-red
• opencv
• python
• pytorch
• raspberry-pi5
• ros2
• yolov8