本研究的目的是選用不同的深度學習架構，並調整超參數項，以優化 AI模型在分割海岸光學影像中不同類型水線的效能，進而透過案例測試，評選出各架構的最優模式，以利於未來海岸研究或應用之所需。本研究進行 AI模型之深度學習所需的光學影像來源，是本研究室先前在台南七股和安平地區的兩段海岸所執行的無人機航拍作業所攝錄的光學影像。為了提高影像中海岸水線之標注資料的準確性，本研究自行設計與開發出一套結合何（2023）改良後的水線偵測演算法與人工操作的半自動化標注軟體，以產生光學影像的對應標籤資料。
本研究選用基於 UNet 的三種模型架構（UNet、UNet++和 DeepUNet）進行深度學習，並利用開源工具 Optuna 來評選出各架構中最具影響力的超參數項―損失函數加權方式，再透過統計分析先決定出各架構的其他影響程度較小的超參數項之初步組合設定。然後將每種架構的初步超參數組合逐一搭配損失函數的五種加權方式，總共得出 15 個模型，再以測試集的評估指標分數檢驗各模型的水線偵測效能，以選出各架構的最優模式：
1. UNet 架構：損失函數為 Focal 加權、批次大小為 2、訓練週期為 150次、學習率為 0.001、優化器為 Adamax。
2. UNet++架構：損失函數為 Focal 加權、批次大小為 2、訓練週期為150 次、學習率為 0.00001、優化器為 Adam。
3. DeepUNet 架構：損失函數為逆頻率加權、批次大小為 4、訓練週期為 99 次、學習率為 0.001、優化器為 Adam。
進一步測試各架構的最優模式對於“複雜水線資料集”與“複雜光源資料集”的水線偵測能力，結果顯示三個最優模式都能夠準確地分辨出水線的位置與類型，且具有良好的泛化能力，顯示出在海岸監測與防災應用具有重要的應用價值。在這三個最優模式中，又以 UNet++的綜合評估指標分數最高。

The purpose of this study is to select different deep learning architectures and adjust hyperparameters to optimize AI model performance in segmenting coastal waterlines in optical images, which were captured by drones along two coastal sections in Qigu and Anping, Tainan. To improve the accuracy of annotated data, a semi-automated annotation software was developed, combining Ho’s (2023) improved waterline detection algorithm with manual operations.
This study employs three UNet-based architectures (UNet, UNet++, and DeepUNet) for deep learning. Using the open-source tool Optuna, the most influential hyperparameter, the loss function weighting method, was selected. Preliminary combinations of other hyperparameters were determined through statistical analysis. Each architecture’s preliminary hyperparameter combinations were paired with five loss function weighting settings, resulting in 15 models. The waterline detection performance of each model was tested using evaluation index scores to select the optimal model for each architecture.
Further testing of the optimal models on "complex waterline" and "complex lighting" datasets demonstrated that all three could accurately identify waterline positions and types, exhibiting good generalization capabilities. This indicates significant application value in coastal monitoring and disaster prevention. Among these, UNet++ with the best hyperparameter set (loss function weighted by Focal, batch size of 2, 150 epochs, learning rate of 0.00001, optimizer Adam) achieved the highest overall evaluation score.

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