IBN-Driven Rip Current Analysis Using UAVs for Next-Generation Coastal Surveillance

The unpredictable nature of rip currents makes them a leading cause of coastal drowning incidents globally. Traditional methods fall short, necessitating an advanced surveillance system that can prioritize critical threats, enable autonomous decision-making with adaptive network control, and optimize resource allocation for enhanced coastal safety. Intent-based networking (IBN) plays a pivotal role in converting high-level intents into automated processes, enabling dynamic control and intelligent resource allocation in critical applications such as the Internet of Things (IoT) and unmanned aerial vehicle (UAV)-based coastal surveillance. This study proposes an artificial intelligence (AI)-powered, IBN-driven framework for coastal surveillance that leverages UAVs and IoT devices to enable real-time rip current analysis through advanced segmentation techniques. In our framework, UAVs with AI-powered IoT systems perform initial rip current analysis using lightweight deep-learning models. High-risk detections are prioritized through the closed-loop feedback mechanism of the IBN and transmitted to control rooms for validation and response, ensuring efficient resource utilization and adaptive surveillance. We expanded the rip current dataset to enhance the segmentation accuracy by incorporating additional samples from open-source platforms and applying diverse environmental conditions. We trained YOLO models and Mask R-CNN, which are suitable for real-time rip current analysis. In addition, we introduced a modified YOLOv11n-seg model, replacing the C3K2 block with C2F and optimizing the channels to reduce the parameters while maintaining accuracy. The best-performing models were tested on edge devices to evaluate the time complexity and reliability.