Standardized AI-Driven UAV Water Rescue System for Safe and Reliable Autonomous Operations

As artificial intelligence (AI) continues to advance, ensuring its trustworthiness, safety, and security in critical applications has become imperative. This study presents an AIdriven UAV-based water rescue system designed under a standardized framework to enhance reliability, robustness, and interoperability. The system integrates AI-powered object detection and autonomous UAV navigation, ensuring high accuracy and efficiency in water rescue operations. Utilizing YOLO-based deep learning models, real-time image recognition, and the Pixhawk flight control system with MAVLink communication, this UAV system operates under rigorous safety and performance standards. The proposed AI quality assurance framework ensures compliance with AI standardization principles, mitigating bias, improving fault tolerance, and promoting global adoption. The system's performance is validated through computational simulations and real-world testing, demonstrating its alignment safety standards and future regulatory frameworks for autonomous UAV operations.