AI-powered computer vision for road defect detection. It analyzes vehicle imagery. Detects, classifies, and quantifies defects like cracks and potholes. Provides metrics on severity and affected area.
Explore FeaturesTwo powerful systems working in harmony for complete road safety management
State-of-the-art deep learning models automatically identify, classify, and quantify road surface defects with precision.
Comprehensive detection and classification of road safety equipment ensuring compliance with safety standards.
Advanced processing infrastructure with intelligent analytics for immediate decision-making and comprehensive reporting.
Enterprise-grade infrastructure powering intelligent road safety
YOLOv8-L & YOLOv11-L: State-of-the-art architectures for real-time object detection and instance segmentation with precision boundary detection capabilities.
YOLOv11n: Lightweight model optimized for road surface classification achieving 97% accuracy with minimal computational overhead.
34,540+ Augmented Images: Comprehensive dataset covering diverse conditions including varied lighting, weather patterns, and road textures across Indian urban environments.
Training: 27,690 | Validation: 3,330 | Testing: 3,520 images
AWS Services: EC2 for computation, S3 for scalable image storage, PostgreSQL for structured data, and MongoDB for flexible document storage.
RESTful APIs with real-time processing endpoints and automated batch capabilities.
Real-Time Analysis: Frame sampling at 45-60 FPS, GPS coordinate extraction using Haversine formulas, and automated defect logging with IST timestamps.
Interactive dashboards with Folium maps and comprehensive analytics.
Transforming road infrastructure management through AI-driven insights
Proactive defect detection identifies hazards before they cause accidents. Continuous monitoring ensures safety equipment presence.
Automated surveys replace manual inspections. Real-time data enables immediate decision-making and resource optimization.
Targeted repairs instead of blanket resurfacing. Precise material quantification reduces waste and prevents costly major repairs.
Eco-friendly material recommendations. Optimized survey routes reduce fuel consumption and carbon footprint.
Trend analysis and performance benchmarking. Predictive maintenance and budget forecasting capabilities.
Versatile solutions for diverse stakeholders
Watch Drishti in action - Real-world demonstration
Complete demonstration of the AI-powered road safety solution in action
Visual insights from our AI-powered road safety solution
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