ROAD SURVEY AI

Inspiration

The idea for this project came from the growing need for efficient, accurate, and automated road condition monitoring. Traditional road surveys are time-consuming, costly, and prone to human errors. AI-driven solutions can automate data collection, analyze road conditions in real time, and provide actionable insights for better infrastructure management.

🏗️ How We Built It Tools & Technologies: We used computer vision, machine learning, and cloud computing to process road images and detect issues like cracks, potholes, and lane markings. Data Collection: Open-source datasets and real-world images were used to train the AI model. AI Model: A deep learning model (CNN-based) was developed using TensorFlow/PyTorch. Deployment: The model was integrated into a web app for easy access, with a simple dashboard for road quality analysis. 🔍 Challenges We Faced Data Availability: Finding high-quality datasets for diverse road conditions was a challenge. Processing Speed: Optimizing the AI model to run efficiently on limited hardware. Accuracy Issues: Ensuring the AI correctly differentiates road damage from shadows and debris. 🌟 What We Learned AI can significantly improve infrastructure monitoring efficiency. Pre-processing images and selecting the right model architecture is crucial for accuracy. Building a user-friendly interface enhances accessibility for city planners and engineers. 📌 Next Steps Expand the dataset with real-time sensor data. Improve AI accuracy with transfer learning and real-time feedback loops. Deploy the system as a mobile app for on-the-go road surveys