Industry: GovTech, Artificial Intelligence
Background
The project aimed to develop an AI-driven solution for efficiently assessing road conditions, identifying anomalies, and providing actionable insights for road maintenance. By automating the process of road inspection and analysis, the goal was to improve road safety, reduce maintenance costs, and optimize resource allocation.
Challenges
Developing a robust AI model to accurately detect and classify road defects in various weather and lighting conditions presented significant challenges. Additionally, processing large volumes of video data efficiently and generating actionable reports required a scalable and efficient infrastructure.
Solution
The project addressed these challenges by:
- Computer Vision Models: Developed AI models capable of detecting potholes, cracks, and other road anomalies in video footage.
- Data Processing Pipeline: Established a pipeline for efficiently processing video data, extracting frames, and applying computer vision models.
- Geolocation Integration: Incorporated GPS data to accurately locate detected anomalies on a map.
- Data Storage and Management: Utilized PostgreSQL to store road condition data, including anomaly details, locations, and images.
- Web Application: Built a web-based interface using Flask and Nginx to visualize road condition data, generate reports, and allow users to interact with the system.
- API Development: Created RESTful APIs to enable integration with other systems and data exchange.
Tech Stack
The project leveraged the following technologies:
- Python: For AI model development, data processing, and backend development.
- Computer Vision Libraries: OpenCV or TensorFlow/Keras for image processing and object detection.
- PostgreSQL: For storing road condition data, including anomaly details, locations, and images.
- Flask: For building the web application backend.
- Nginx: For serving static content and routing requests.
- Django: For potential future expansion of the web application.
Key Outcomes
The AI-powered road condition assessment system delivered the following benefits:
- Improved Road Safety: Identified potential hazards like potholes and cracks to prevent accidents.
- Optimized Maintenance: Provided data-driven insights for prioritizing road repairs and maintenance.
- Cost Reduction: Reduced manual inspection efforts and optimized resource allocation.
- Data-Driven Decision Making: Enabled data-driven decision-making for road infrastructure management.
