Deep Learning-Based Waterline Detection Applied to Wave Period Measurement in the Nearshore Swash Zone

This study proposes an integrated framework combining aerial photography of unmanned aerial vehicle (UAV), AI-based waterline detection, and a rigorous quality control (QC) scheme for estimating wave periods in the swash zone. The proposed approach automatically extracts instantaneous waterlines from high-resolution UAV videos and converts them into wave series using timestack analysis. The DeepUNet model achieved a pixel-level recognition score of 75.0% for both F1-score and Dice, demonstrating reliable performance in detecting thin waterline features. The integration of spatial and temporal QC further improves the robustness of waterline tracking and reduces false detections. Wave periods derived from wave series across different cross-sections in the swash zone exhibit spatial consistent and qualitative consistency when contextually compared with offshore data buoy observations, while the quantitative differences reflect variation in nearshore wave dynamics. These results confirm the feasibility and effectiveness of the proposed framework for high-resolution nearshore wave monitoring.