비디오 모니터링 데이터를 활용한 U-Net 기반 Waterline추출 자동화 및 해안선 추정

초록

Coastal erosion has caused significant ecological damage and substantial social and economic losses, emphasizing the necessity for long-term monitoring of shorelines. Video monitoring systems have emerged as effective tools for observing shorelines changes. However, the shoreline extracted from video monitoring systems represents the waterline, defined as the boundary where waves reach the beach. The extraction of waterlines using these systems often requires manual parameter adjustments to account for dynamic coastal environments. Furthermore, the extracted waterline is influenced by variations in waves and currents, posing challenges for consistent and reliable monitoring. To address these issues, this study utilizes the deep learning model ‘U’-shaped Neural Network (U-Net) to analyze images of Chuam Beach. A dataset comprising 204 pairs of Time-exposure (TimeX) images was developed to train the U-Net model, which was subsequently utilized to propose an automated method for waterline extraction. Using the extracted waterlines, the beach slope was calculated, enabling the estimation of a tide-based shoreline unaffected by wave and currents variations. Comparative analysis with UAV (Unmanned Aerial Vehicle)-based shoreline data revealed a maximum vertical error of 0.18 m for the waterline and 0.27 m for the shoreline, demonstrating high accuracy. These findings indicate that the U-Net model effectively and accurately detects waterlines, while the proposed shoreline estimation method ensures consistent monitoring of shoreline changes, including advances and retreats.

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