運用無人機及影像套合法進行混凝土橋梁裂縫檢測

、線上人數：61

、訪客IP：3.22.79.125

姓名	李宣儀(Hsuan-Yi Li) 查詢紙本館藏	畢業系所	土木工程學系
論文名稱	運用無人機及影像套合法進行混凝土橋梁裂縫檢測 (Concrete Bridge Crack Size Measurement based on Unmanned Ariel Vehicle and Image Registration)
檔案	[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] [檢視] [下載] 本電子論文使用權限為同意立即開放。已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
摘要(中)	裂縫的尺寸是決定混凝土橋梁壽命的關鍵要素之一。當前，傳統橋梁檢測仰賴檢測員利用梯子或纜繩攀爬橋梁進行作業，不僅相當耗時且費力，此方法的安全性也有些疑慮。為改善此問題，近期有許多研究自製機械手臂加載相機並透過影像處理偵測及量測裂縫。然而，機械手臂所費不貲。因此，本研究設計並實驗一套運用無人機承載高解析數位相機檢測橋梁裂縫的方法。具體而言，本研究透過空間三角平差將涵蓋地面控制點的遠拍影像及具備清楚裂縫特徵的近拍影像透過影像間的共軛點將所有影像套合在一個絕對坐標系統。其中，本研究利用三種方法產製共軛點:(1)人工點選橋梁現有特徵作為共軛點。(2)隨機挑選由 Scale-Invariant Feature Transform(SIFT) 產生的共軛點。(3)隨機挑選由 SIFT 產生的起始點再以 ERDAS Leica PhotogrammetrySuite(LPS)提供之匹配流程(AutomaticTieGeneration)產製共軛點。在確立絕對坐標系統後，所有的數位影像將被重新採樣為每像元 0.1 毫米的正射化影像來進行後續的裂縫量測，並運用人工量測及反曲點辨識裂縫邊界的兩種方法進行量測比對。本研究在桃園市楊梅區愛鶴橋進行現地實驗。與測驗員的量測數據相比，以人工產製共軛點並量測裂縫的方展現最佳成果，可達到 0.18 毫米的均方根誤差、0.15 毫米的平均誤差、25.41百分比的相對誤差。而結合 SIFT 和 LPS 自動化產製共軛點及人工量測裂縫的方法達到 0.46 毫米的均方根誤差、0.4 毫米的平均誤差、72.42 百分比的相對誤差。
摘要(英)	Crack development is a clear indicator to the durability of concrete bridges. Traditional bridge inspections which rely inspectors to climb on bridges with lift cars are not only unsafe for inspectors, but also time and labor consuming. While there have been some researches designing robotic arms carrying digital cameras and applying image processing for crack detection, robotic arms are expensive. Therefore, this research proposes a solution that applies unmanned aerial vehicles (UAV) and high-resolution digital cameras to measure concrete bridge cracks. To be specific, two types of images are taken, which are close-up images that can observe cracks more clearly and long-range images that covers ground control points. Afterward, we register these two types of images to establish the absolute coordinate system with ground control points and tie points through the block triangulation. This research examines three approaches of generating tie points: (1) Manually select tie points with features on the bridge such as nails and dots. (2) Randomly input tie points generated from Scale- Invariant Feature Transform (SIFT). (3) Randomly input tie points generated from SIFT as initial tie points and perform automatic tie generation with Erdas Leica Photogrammetry Suite (LPS) image matching module (Automatic Tie Generation). Afterward, close-up images are processed into ortho-rectified images with 0.1mm pixel size for crack size measurement. Crack sizes are finally determined by a manual measurement approach and an inflection point approach for comparison. An experiment was conducted on Ai He concrete bridge located in Yangmei District, Taoyuan City, Taiwan. By comparing to in-situ measurements from three surveyors, the proposed solution that manually selects tie points and measure crack sizes produces the best result with 0.18 mm root mean square difference (RMSD), 0.15 mm mean difference (MD), and 25.41% mean relative difference (MRD). Furthermore, manual crack size measurement with automatic tie point generation with SIFT and LPS achieve 0.46 mm RMSD, 0.4 mm MD, and 72.42% MRD.
關鍵字(中)	★ 無人機 ★ 影像套合 ★ 混凝土 ★ 裂縫 ★ 檢測	關鍵字(英)	★ Unmanned aerial vehicle ★ image registration ★ concrete ★ crack ★ measurement
論文目次	摘要 ......................................................................................................................................II ABSTRACT ........................................................................................................................ IV LIST OF TABLES..............................................................................................................VII LIST OF FIGURES .......................................................................................................... VIII 1. 2. INTRODUCTION........................................................................................................... 1 1.1 BACKGROUND ........................................................................................................... 1 1.2 CONCRETE BRIDGE CRACK INSPECTION WITH UAV ..................................................... 3 1.3 RESEARCH OBJECTIVE ............................................................................................... 5 1.4 SUMMARY ................................................................................................................. 5 METHODOLOGY .......................................................................................................... 7 2.1 INSTRUMENT ............................................................................................................. 7 2.2 WORKFLOW............................................................................................................... 8 2.3 CAMERA CALIBRATION.............................................................................................. 9 2.4 IMAGE TAKING ........................................................................................................ 11 2.5 IMAGE ORIENTATION AND REGISTRATION ................................................................. 12 2.5.1 Scale-Invariant Feature Transform (SIFT) ........................................... 13 2.5.2 Automatic tie point generation in LPS .................................................. 16 2.6 ORTHORECTIFICATION ............................................................................................. 17 2.7 CRACK WIDTH MEASUREMENT ................................................................................. 18 RESULTS .................................................................................................................... 20 3.1 IMAGE ORIENTATION AND REGISTRATION ................................................................. 20 3.2 ORTHORECTIFICATION ............................................................................................. 23 3.3 CRACK SIZE MEASUREMENT ..................................................................................... 26 3.4 SUGGESTIONS .......................................................................................................... 32 CONCLUSIONS ......................................................................................................... 34 FUTURE WORK ........................................................................................................ 35
參考文獻	[1] Adhikari, R. S., Moselhi, O., Bagchi, A., 2013. Image-Based Retrieval of Concrete Crack. Properties for Bridge Inspection, Automation in Construction, 39, pp. 180-194. [2] ACI 224R-90, 2008. Control of Cracking in Concrete Structures, America Concrete Institute: Farmington Hills, MI, USA. [3] Blaschke, T., 2010. Object based image analysis for remote sensing, ISPRS journal of photogrammetry and remote sensing ,65, pp. 2-16. [4] Bhatta, B., 2012. Urban Growth Analysis and Remote Sensing: A Case Study of Kolkata, India 1980–2010. [5] Duque, L., 2017. UAV-Based Bridge Inspection and Computational Simulations. Theses and Dissertations. 2159. [6] Eschmann, C., Wundsam, T., 2017. Web-Based Georeferenced 3D Inspection and Monitoring of Bridges with Unmanned Aircraft Systems, Journal of Surveying [7] Fraser, C. S., 1997. Digital Camera Selif-calibration, ISPRS Journal of Photogrammetry and Remote Sensing, 52(4), pp. 149-159. [8] Förstner, W., Gülch, D., 1987. A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centres of Circular Features, International Archives of the Photogrammetry, Remote Sensing and Spatial Sciences, Intercommission Workshop. Interlaken, Switzerland: ISPRS. [9] Gupta, R., 2015. Four key GPS Test Consideration for Drone and UAV Developers. Spirent. [10] Gini, R., Pagliari, D., Passoni, D., Pinto, L., Sona, S., Dosso, P., 2013. UAV PHOTOGRAMMETRY: BLOCK TRIANGULATION COMPARISONS, International Archives of the Photogrammetry, Remote Sensing and Spatial Sciences, XL-1/W2, pp. 317-323. Rostock, Germany: ISPRS. [11] Hirschmuller, H., 2008. Stereo processing by semiglobal matching and mutual information. IEEE Transactions on pattern analysis and machine intelligence, 30, pp. 328-341. [12] Hallermann N. & Morgenthal G., 2014, Visual inspection strategies for large bridges using Unmanned Aerial Vehicles (UAV). Proc. of 7th IABMAS, International Conference on Bridge Maintenance, Safety and Management, Shanghai, pp.661-667. Engineering, 143(3), pp. 04017003-1- 04017003-10. [13] iWitness. iWitnessPRO Photogrammetry Software. Available online: https://www.iwitnessphoto.com/iwitnesspro_photogrammetry_software/ (accessed on 25 March, 2019) [14] Kim, H., Lee ,J., Ahn ,E., Cho ,S., Shin ,M., Sim ,S.H., 2017.Concrete Crack Identification Using a UA V Incorporating Hybrid Image Processing, Sensors, 17, 2052, 10.3390/s17092052. [15]Lang, S., Albrecht, F., Blaschke, T., 2011. OBIA- Tutorial,Centre for Geoinformatics(Z_GIS). Paris-Lodron University Salzburg. [16]Lowe, D. G. (2004). Distinctiveimage features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91-110. [17] LPS Manager, 2010. Introduction to photogrammetry. User’s guide. [18] Leprince, S., Barbot, S., Ayoub, F., Avouac, J.P., 2007. Automatic and Precise Orthorectification, Coregistration, and Subpixel Correlation of Satellite Images, Application to Ground Deformation Measurements. Geoscience and Remote Sensing, IEEE Transactions, 45, pp.1529 – 1558. [19] Laliberte, A.S., 2010. Acquisition, orthorectification, and object-based classification of Unmanned Aerial Vehicle (UAV) imagery for rangeland monitoring, Photogrammetric Engineering and Remote Sensing, 76, pp. 661-672.Li, Y., Li, H., & Wang, H., 2018. Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application. Sensors, 18(9), 3042. [20] Ministry of Transportation and Communications, 2018. The maintenance and inspection regulation of railway, The regulation of the standard in engineering department, Ministry of Transportation and Communications for railways, Taiwan. [21] Mohan, A., Poobal, S., 2018. Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal, 57(2), pp.787-798. [22] Muhammad O., Lee, M., Mojgan, H.M., Hewitt, S., Parwaiz, M., 2019. Use of gaming technology to bring bridge inspection to the office. Structure and Infrastructure Engineering, 15(10), pp.1292-1307. [23] National Academies of Sciences, Engineering, and Medicine, 2017. Control of Concrete Cracking in Bridges. Washington, DC: The National Academies Press. [24] Oh, J-K., Jang, G., Oh, S., Lee, J. H.,Yi, B-J, Moon, Y. S., Lee, J. S., Choi, Y. (2009). Bridge inspection robot system with machine vision, Automation in Construction, 18, 929- 941. [25] Rau, J.Y., Hsiao, K.W., Jhan, J.P., Wang, J.C., Fang, W.C., Wang, J.L., BRIDGE CRACK DETECTION USING MULTI-ROTARY UAV AND OBJECT-BASE IMAGE ANALYSIS, Commission, VI, WG VI/4. [26] Remondino,F., El-Hakim, S.F., Gruen, A., and Zhang, L., 2008. Turning images into 3-D models, IEEE Signal Processing Magazine, 25 (4) , pp. 55-65. [27] Rehak, M., Mabillard, R., & Skaloud, J., 2013. A Micro-UAV with the Capability of Direct Georeferencing, International Archives of the Photogrammetry, Remote Sensing and Spatial Sciences, XL-1/W2, pp. 317-323. Rostock, Germany: ISPRS. [28] Sutter, B., Arnaud, L., Pham, M.T., Gouin, O., Jupille, N., Kuhn, M., Lulé, P., Michaud, P., Rémy, P., 2018, A semi-autonomous mobile robot for bridge inspection, Automation in Construction, 91, pp.111-119. [29] Sankarasrinivasan, S., Balasubramanian, E., Karthik ,K., Chandrasekar U. and Gupta R., 2015. Health Monitoring of Civil Structures with Integrated UAV and Image Processing System, procedia Computer Science, 54, pp. 508 – 515. [30] Sun, Y.L., Wang, J., 2016. Performance Analysis of SIFT Feature Extraction Algorithm in Application to Registration of SAR Image, MATEC Web of Conferences, 44, 01063. [31] Sauvola, J., Pietikäinen, M., 2000. Adaptive document image binarization. Pattern Recognition, 33(2), pp. 225-236. [32] Tahar, K.N., Kamarudin, S.S., 2016. UAV ONBOARD GPS IN POSITIONING DETERMINATION, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B1, pp.1037-1042. Prague, Czech Republic: ISPRS. [33] Wang, Y., 1998. Principles and applications of structural image matching, Journal of Photogrammetry and Remote Sensing, 53(3), pp. 154-165. [34] Yang, Y.S., Wu, C.L., Hsu, T.C., Yang, H.C., Lu, H.J., Chang, C.C., Image analysis method for crack distribution and width estimation for reinforced concrete structures, Automation in Construction, 91, pp.120-13.
指導教授	黃智遠王仲宇	審核日期	2020-7-31
推文	facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu
網路書籤	Google bookmarks del.icio.us hemidemi myshare

博碩士論文 108322091 詳細資訊