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研究生:鍾維倫
研究生(外文):ZHONG,WEI-LUN
論文名稱:建築物航拍建模精度改善及其於判定建築物倒塌模式應用之研究
論文名稱(外文):A Study on the Accuracy Improvement of 3D Models of Buildings Created with Aerial Photogrammetry and itsApplication in Determining Building Collapse Mode
指導教授:童士恒
指導教授(外文):TUNG,SHIH-HENG
口試委員:蕭漢威林雪淳
口試委員(外文):HSIAO,HAN-WEILIN,HSUEH-CHUN
口試日期:2020-07-28
學位類別:碩士
校院名稱:國立高雄大學
系所名稱:土木與環境工程學系碩士班
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:123
中文關鍵詞:無人飛行載具建築物數位影像相關係數法尺度不變轉換特徵特徵點即時動態定位控制點三維數值模型
外文關鍵詞:UAVBuildingDigital Image Correlation MethodScale-Invariant Feature Transform MethodFeature PointReal-Time Kinematic TechniqueControl Point3D Model
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台灣位於太平洋地震帶,地震頻傳,常造成人民財產及生命的損失,所以救難行動就顯得格外重要。無人飛行載具(UAV)可於空中進行航照影像的拍攝,且有著機動性高、危險性低的特色,故於近幾年受到廣大的重視。本研究擬以三維數值模型判斷建物傾斜倒塌的模式,並以數位影像相關係數法(DIC)進行控制點定位,改善三維數值模型之精度。
本研究先拍攝各類建物之影像,透過尺度不變特徵轉換(SIFT)找出大部分建物上皆有的特徵點,將其當作建物的基本資料,結果顯示特徵點大部分出現於窗框及冷氣機上,較不易出現於建物本體上。以UAV航拍建物影像,透過即時動態測量RTK量測控制點之三維座標,並以全測站量測建物上特徵點之三維座標,利用建立出的三維數值模型,與地面檢核點、建物上檢核點比較出模型精度,結果顯示,地面檢核點水平精度約4.4公分、高程精度約15.9公分,建物上檢核點水平精度不超過7公分、高程精度約7.6公分,顯示空中三角量測法應用於建物上有其可靠性。
本研究為模擬建物倒塌樣貌,以自製模型進行實驗,建立出15種模型傾斜後特徵點的空間座標,透過三維數值模型上特徵點座標進行比對,藉以找出較相關的倒塌模式;在建物實際倒塌時常伴隨著結構體的破壞、剝離,實驗也將探討特徵點有缺漏時,對判定模式之影響。實驗結果顯示此判定法應用於不同方向上的旋轉皆有效,且角度不同依舊能夠判定出來,在有特徵點缺漏時,此判定方法依舊可行。後續為了達成自動判定倒塌模式之目的,可以先透過SIFT自動找取倒塌建築物上出現的特徵點,再使用這些點位的座標將其用於倒塌模式的判定上,這樣可以在倒塌模式判定上達到自動化的成果。
接著本研究為增進三維數值模型之精度,故比較人工選點、DIC選點與DIC逐層匹配選點之精度差異,與控制點標記影像數量對檢核點精度之影響。實驗結果顯示,標記影像數量會影響檢核點精度,且DIC能夠改善現地控制點定位精度,且僅需標記3張影像,其精度就能達到人工選點5張之精度,其中DIC逐層匹配選點之精度為三種選點方式中最好的。

Taiwan is located on the Pacific Seismic Belt. Earthquakes often cause loss of people’s properties and lives, so rescue operations are essential. Unmanned aerial vehicles (UAV) can take aerial photos and have the characteristics of high flexibility and low risk, so they have received considerable attention in recent years. This study intends to use the three-dimensional numerical model to judge the collapse mode of the building and use the Digital Image Correlation Method (DIC) to locate the control points to improve the the 3D model’s accuracy.
In this study, we took images of various buildings at first, found the common feature points on most buildings through Scale-Invariant Feature Transform Method (SIFT), and used them as the basic data of the buildings. The results showed that most of the feature points appeared at the window frame and air conditioners. It is less to appear on the structure surface of the building. After that, UAV is used to catch the aerial photographs of building structures, measuring the three-dimensional coordinates of control points through Real-time Kinematic (RTK) technique, and then measuring the three-dimensional coordinates of feature points on the building through the total station. The checkpoint coordinates obtained from RTK and 3D model are compared to determine the 3D model’s accuracy. The results show that the ground checkpoint’s horizontal accuracy is about 4.4 cm, and the elevation accuracy is about 15.9 cm. The horizontal accuracy of the checkpoint on the building is less than 7 cm, and the elevation accuracy is about 7.6 cm. It shows that the Aerotriangulation has its reliability when applied to buildings.
In this study, to simulate the collapse of a building, the experiment was carried out with a self-made model. Fifteen kinds of spatial coordinates of feature points after tilting are established, and the coordinates of the feature points on the 3D model are compared to find out the most relevant collapse mode. When the building collapsed, it is often accompanied by the destruction and peeling of the structure. The experiment will also explore the impact of the missing feature points on the judgment mode. Experimental results show that this judgment method is effective for rotation in different directions, and different angles can still be judged. When there are missing feature points, this judgment method is still feasible. In order to achieve the purpose of automatically identifying the collapse mode, SIFT Method can be used to automatically find the feature points that appear on the collapsed building. And then, we can use the coordinates of these points to determine the collapse mode. This can achieve the results in identifying collapse mode automatically.
This study compares the different ways to locate control points to improve the accuracy of the 3D model. One of the positioning methods is done by manual, another is using DIC, and the other is using DIC to match point layer by layer. This study also explores the influence of the number of the images including the control points on the accuracy of checkpoints. Experimental results show that the number of marked images will affect the accuracy of checkpoints. DIC can improve the positioning accuracy of control points. We can achieve the same 3D model’s accuracy by positioning the control point on three images using DIC while manually positioning it on five images. Among them, the accuracy of DIC matching point layer by layer is the best among the three point selection methods.

目錄
圖目錄
表目錄
中文摘要
英文摘要
第一章 前言
1.1 研究動機
1.2 研究目的
第二章 文獻回顧
2.1 尺度不變特徵轉換(SIFT)
2.2 衛星定位
2.3 空中三角測量
2.4 數位影像相關係數法(DIC)
2.5 文獻總結
第三章 研究方法
3.1 尺度不變轉換特徵
3.2 數位影像相關係數法
3.3 虛擬基準站-即時動態定位
3.4 Pix4Dmapper
第四章 實驗驗證
4.1 以SIFT匹配取得建物之特徵點
4.1.1 實驗儀器
4.1.2 實驗步驟
4.1.3 實驗結果
4.1.4 結果與討論
4.2 以空中三角測量方法量測建立特徵點空間座標
4.2.1 實驗器材與配置
4.2.2 實驗步驟
4.2.3 實驗結果
4.2.4 結果與討論
4.3 以空間座標轉換判定建物倒塌模式
4.3.1 模型介紹
4.3.2 實驗儀器與配置
4.3.3 實驗步驟
4.3.4 實驗結果
4.3.5 討論
4.4 比較檢核點位置選取之差異
4.4.1 實驗步驟
4.4.2 實驗結果
4.4.3 結果討論
4.5 應用DIC改善控制點標記
4.5.1 控制點與檢核點
4.5.2 實驗步驟與結果
4.5.3 結果與討論
第五章 結論與建議
5.1 結論
5.2 建議
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