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研究生: 卓慧丞
Cho, Hui-Cheng
論文名稱: 應用卷積神經網路於碎波角分析之研究
Research on Applying Convolution Neural Networks to Peel angle analysis
指導教授: 董東璟
Doong, Dong-Jiing
學位類別: 碩士
Master
系所名稱: 工學院 - 水利及海洋工程學系
Department of Hydraulic & Ocean Engineering
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 57
中文關鍵詞: 碎波角深度學習卷積神經網路衝浪
外文關鍵詞: Peel angle, Deep Learning, Convolutional Neural Networks, Surfing
相關次數: 點閱:62下載:7
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  • 碎波角(Peel angle)(亦稱浪崩角)是兩不同時間的碎波邊界連線與碎波前進方向之夾角,是評估衝浪活動適宜性的一個參數,本研究旨在建立一個自動分析碎波角的模式。
    本文採用深度學習方法裡的卷積神經網路(Convolutional neural network, CNN)來分析碎波角,本研究透過模式超參數的率定來提高辨識準確率,結果顯示CNN模式中之激活函數選擇Sigmoid函數,並使用2000次迭代以及64的批次大小可獲得最佳模式。本研究分析三個現場觀測海域共30部空拍影片(影像時序列),對於碎波帶的判識準確性達80%以上,而對於碎波角的分析,獲得其平均誤差為5.9o,在可以接受之範圍。除此之外,考量便利性,本研究亦提出以單張影像取代影像時序列來計算碎波角,結果發現,單張影像與前述影像時序列分析結果相近,兩者誤差約3.9o,此結果確認未來可以單張影像來進行分析。
    最後,本文分析台灣10處衝浪熱點海域的碎波角,結果發現各地的碎波角約介於30o~70o之間,其季節變化並不顯著,分析也發現部分海域受海岸地形影響,碎波角有較大空間變異性,民眾從事相關活動時宜留意。

    Peel angle is the angle between the boundary line of the breaking wave and the advanced direction of the breaking wave at two separate times. It is a parameter used to assess the suitability of surfing activities. This research intends to build a model for the automatic analysis of peel angles.
    In this study, the Convolutional Neural Network (CNN) is used to calculate the peel angel. The calibration of the model hyperparameters has been implemented to increase the identification precision. The results indicate that the optimal CNN mode can be obtained by using the Sigmoid activation function,2000 iterations, and a batch size of 64. In three on-site observation sea areas, a total of 30 aerial videos (time-series images) were analyzed, and the identification accuracy of the surf zone was greater than 80%. The average error for the analysis of the peel angle was 5.9 degrees. In addition, replacing time-series images with a single image is proposed in this study in order to determine the angle of breaking wave. The results indicate that the picture and video analysis results are comparable, with a difference of approximately 3.9 degrees. The results of the study confirm that the analysis method of a single image is feasible.
    Eventually, the peel angle of ten surfing spots in Taiwan is analyzed in this study. The results indicate that peel angles range between 30 and 70 degrees, and seasonal variations are negligible. The investigation also revealed that certain coastal regions are affected by topography and the spatial distribution of the peel angle is different. Therefore, the public should take precautions when engaging in associated activities.

    摘要 i ABSTRACT ii 致謝 viii 目錄 x 表目錄 xiii 圖目錄 xiv 第一章 緒論 1 1-1研究背景 1 1-2文獻回顧 3 1-3研究目的 5 1-4研究架構 6 第二章 研究區域與資料 7 2-1 研究區域 7 2-2 CNN模式訓練與驗證資料 7 2-3碎波角資料 9 第三章 研究方法 10 3-1 碎波角定義 10 3-2 卷積神經網路(CNN) 12 3-3 模式建置 14 3-3-1 模式架構 16 3-3-2 評估指標 16 3-3-3二元分類混淆矩陣 17 3-3-4 二元分類評估指標 17 3-3-5交併比(Intersection over Union, IOU) 18 3-4碎波角分析 19 3-4-1時序列影像 19 3-4-2時間步長 22 3-4-3評估指標 23 3-5單張影像 24 第四章 研究成果與探討 25 4-1 模式訓練 25 4-1-1激活函數 25 4-1-2 迭代次數 27 4-1-3 批次大小 29 4-1-4 訓練結果 31 4-1-5超參數探討 32 4-2 碎波判釋驗證 35 4-2-1 空拍機影像 35 4-2-2 衛星影像 37 4-2-3 不同仰角 38 4-3 碎波角驗證 41 4-3-1 時序列影像 41 4-3-2 單張影像 46 4-4 碎波角特性分析 49 第五章 結論與建議 52 5-1結論 52 5-2建議 53 參考文獻 54

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