evegrace/AI-Driven-Accident-Detection-and-Emergency-Response-Uganda

AI-Driven Accident Detection and Emergency Response for Low Resource Settings A Case Study from Uganda # AI-Driven-Accident-Detection-and-Emergency-Response-Uganda AI-Driven Accident Detection and Emergency Response for Low Resource Settings A Case Study from Uganda An AI-driven accident detection and alert system designed for low-resource settings. We are using a computer vision–based accident detection from roadside cameras with a hospital-capacity–aware routing and multi-channel alerting framework tailored to Uganda’s emergency ecosystem. The system employs convolutional neural networks with temporal anomaly detection to recognize motorcycle crashes, integrates OpenStreetMap and Ministry of Health data to identify the nearest trauma-ready hospital, and dispatches alerts through SMS/WhatsApp APIs. ## Features - Real-Time Accident Detection: Implements YOLO for precise and quick detection of road accidents. - Twilio API Integration: Sends emergency alerts through SMS and calls. - Location Integration: Captures and sends the exact location of the accident using GPS. - Scalable Backend: Processes accident data and manages communications efficiently. - User-Friendly Dashboard: Visualizes data and system operations for easier monitoring.