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姓名 曾得瑋(Te-Wei Tseng) 查詢紙本館藏 畢業系所 土木工程學系 論文名稱 利用機器學習於邊坡光學與紅外線熱成像融合之長期穩定性監測評估
(The evaluation of slope monitoring using optical and thermal images fusion through machine learning)相關論文 檔案 [Endnote RIS 格式]
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至系統瀏覽論文 (2025-8-1以後開放)
摘要(中) 山坡地的變化和移動量始終為其是否會坍塌的重要因素，以安全 性高、安裝成本低、機動性高的數位攝影測量方式，長時間地對山坡 地進行拍攝及觀測，可以有效地確保山坡地的穩定性。
本研究旨在將光學成像技術、紅外線熱成像技術與機器學習方法結合，並且用於雙時段圖像的變化檢測上。利用微型單板電腦—樹梅派結合兩鏡頭模組，通過在現場同一位置但不同時間拍攝的圖像，以機器學習的方式，讓電腦預測雙時段光學圖像組中的變化位置;以數值處理的方式，讓電腦計算雙時段紅外線熱圖像組中的溫度變化。
本研究先經室內模型實驗，使用自行收集的圖像組，訓練機器學習模型，以此對變化檢測模型進行可行性分析和檢驗，後再將監測系統移至室外，在現有坡地旁架設測站，根據監測結果顯示，本研究之方法能夠有效地在室外環境下進行長時間地監測作業，在國立中央大學停車場變化檢測的 mean F1-score 和 mean IoU 分別能達到 0.9359 和 0.8850，而在新北市瑞芳區南雅里的成效良好。摘要(英) The change and displacements of slopes has always been an important
factor in determining whether they will collapse. Digital photogrammetric
methods, which are characterized by high safety, low installation cost, and
high mobility, can effectively ensure the stability of slopes by continuously
capturing and observing them over a long period of time. This study aims
to combine optical imaging technology, infrared thermography, and
machine learning methods for change detection in dual-temporal images.
By utilizing a micro single board computer, specifically the Raspberry Pi,
in combination with two camera modules, images are captured at the same
location but at different times. Through machine learning, the computer
can predict the locations of changes in the bi-temporal optical image set.
Numerical processing is used to calculate temperature changes in the bi-
temporal infrared thermographic image set. Initially, indoor model
experiments were conducted using a self-collected set of images to train
the machine learning model and analyze the feasibility and effectiveness
of the change detection model. Subsequently, the monitoring system was
deployed outdoors, setting up a monitoring station near existing slopes.
The monitoring results demonstrated that the proposed method in this study
can effectively perform long-term monitoring operations in outdoor
environments. The change detection score of mean F1-score and mean IoU
in the parking lot of National Central University can reach 0.9359 and
0.8850 respectively, showcasing good performance in the Nanya Village,
Ruifang District, New Taipei City.關鍵字(中) ★ 光學成像
★ 紅外線熱成像
★ 機器學習
★ 變化檢測
★ 邊坡災害防治關鍵字(英) ★ Optical imaging
★ Infrared thermal imaging
★ Machine learning
★ Change detection
★ Slope monitoring論文目次 摘要............................................................................................................. i Abstract..................................................................................................... ii
目錄........................................................................................................... iv
圖目錄....................................................................................................... vi
表目錄..................................................................................................... xvi
1. 緒論.....................................................................................................1
1.1. 研究背景 ..................................................................................................... 1
1.2. 研究目的 ..................................................................................................... 3
1.3. 研究架構 ..................................................................................................... 3
2. 文獻回顧.............................................................................................5
2.1.滑坡監測技術回顧 ..................................................................................... 5
2.1.1.滑坡行為與傳統監測技術.............................................................................. 5
2.1.2.攝影測量於邊坡變化檢測............................................................................ 10
2.2.紅外線熱成像 ........................................................................................... 22
2.2.1.紅外線原理概述............................................................................................ 22
2.2.2.紅外線熱成像於土石結構應用.................................................................... 26
2.3.機器學習於影像分析應用 ....................................................................... 33
2.3.1.機器學習概述................................................................................................ 33
2.3.2.機器學習於影像判釋應用............................................................................ 44
2.3.3.機器學習變化檢測方法資料選擇................................................................ 52
2.4.綜合評析 ................................................................................................... 57
3. 研究方法...........................................................................................59
3.1.研究流程 ................................................................................................... 59
3.2.儀器設備組建 ........................................................................................... 60
3.2.1.硬體選擇........................................................................................................ 61
3.2.1.1.核心系統 ................................................................................................... 63
3.2.1.2.軌道系統 ................................................................................................... 69 3.2.1.3.供電系統 ................................................................................................... 75
3.2.2. 外殼設計........................................................................................................ 78 3.2.2.1. 中心電子設備設計外殼 ...........................................................................79 3.2.2.2. 周圍設備設計外殼 ...................................................................................86
3.2.3. 圖像變化檢測................................................................................................ 90
3.2.3.1.光學成像 ...................................................................................................90 3.2.3.1.1.變化檢測模型............................................................................................ 90
3.2.3.1.2.混淆矩陣.................................................................................................. 100
3.2.3.2.紅外線熱成像 .........................................................................................107 3.2.4. 操作腳本.......................................................................................................110 3.2.4.1. 操作腳本流程 ......................................................................................... 110
3.2.4.2.拍攝與移動 ............................................................................................. 111
3.2.4.3.資料管理 ................................................................................................. 113
3.2.4.4.光學成像處理 ......................................................................................... 116
3.2.4.5.熱成像處理 ............................................................................................. 119
3.3. 模型模擬 ................................................................................................. 121 3.3.1. 遙測公開影像資料...................................................................................... 121 3.3.1.1. OSCD ......................................................................................................121 3.3.1.2. LEVIR-CD.............................................................................................. 123 3.3.2. 逆向坡縮尺模型實驗.................................................................................. 124 3.3.2.1. 破壞準則與材料選用 .............................................................................124 3.3.2.2. 實驗說明 .................................................................................................128 3.4現地監測 ................................................................................................. 134
3.4.1中央大學停車場.......................................................................................... 134
3.4.2南雅場址...................................................................................................... 144
4. 研究結果.........................................................................................153
4.1. 儀器設備硬體和外殼 ............................................................................. 153
4.2.模型模擬結果 ......................................................................................... 162
4.2.1.遙測公開影像資料................................................................................. 162
4.2.1.1.OSCD ......................................................................................................162
4.2.1.2.LEVIR-CD.............................................................................................. 168
4.2.2. 逆向坡縮尺模型實驗.................................................................................. 176
4.3.現地監測 ................................................................................................. 184
4.3.1. 中央大學停車場.......................................................................................... 184
4.3.2.南雅場址...................................................................................................... 198
5. 結論與建議 ....................................................................................213
5.1. 結論 ......................................................................................................... 213
5.2. 建議 ......................................................................................................... 214
參考文獻.................................................................................................217
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陳祺杰(2019)，地震引致岩坡滑動之破壞機制與變形特徵，博士論文， 國防大學理工學院國防科學研究所環境資訊及工程組。指導教授 鐘志忠(Chih-Chung Chung) 審核日期 2023-7-28 推文 plurk
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