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以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數：15 、訪客IP：13.59.136.170
姓名 何庭武(Ting-Wu Ho) 查詢紙本館藏 畢業系所 土木工程學系 論文名稱 應用資料探勘分群分類演算法與空間資料庫技術在鋪面裂縫影像辨識之初探
(Pavement Distress Image Recognition Using Clustering and Classification Algorithms and Spatial Database Techniques)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 最近幾年來汽機車的數量在世界各地快速成長，相對的公路等市區道路逐漸增加，需要定期對道路路面的狀況進行調查，路面缺陷的損壞是影響路面狀況關鍵的因素，若能在路面缺陷出現的初期就發現並追蹤其破壞狀況，從中對路面維護進行合理的決策和評估，道路的維護成本等費用將大幅的降低，但是如何在不影響車流的情況下，準確的發現並辨識路面缺陷已經成為待解決的一大難題。傳統的路面缺陷調查工作主要仰賴人工方式調查為主，其消耗的人力和時間以及對於交通的影響和調查人員的安全性問題，又無法得到客觀的評估，而造成路面缺陷檢測結果不一致等缺點，已經難以對應道路快速成長的需求，因此，為了減少人工檢測的成本與時間，將檢測過程以自動化方式來實施。
本研究提出一種基於資料探勘分析與處理的自動化路面檢測方法：首先進行路面缺陷影像的資料前處理，再將影像資料轉換成分群資料的資料結構，並使用分群演算法進行路面缺陷的萃取，進而得到路面缺陷的群集資料，之後使用分類演算法辨識出缺陷的種類，並將結果儲存於裂縫資料庫。另外，本研究提出裂縫合併演算法與裂縫連結演算法針對裂縫資料庫進行相關的處理，前者將相鄰的裂縫資料合併為一個裂縫群組，後者將同一位置但不同時間的裂縫群組互相連結，經由以上兩個演算法方可記錄其裂縫在不同時間的變化，為路面檢測提供有效的依據，有利於路面維護和管理，幫助檢測人員進行合理的決策和評估。
摘要(英) The pavement maintenance of a road today relies mainly on manual pavement condition inspection and distress rating, and this manual method is costly, labor-intensive, time-consuming, and dangerous to construction workers and may affect traffic flow. Moreover, such the manual method is very subjective and may have a high degree of variability, being unable to provide meaningful information. Additionally, because only a small area of the road surface can be sampled by using the manual method, it may result in relatively low accuracy of pavement distress information. Hence, an automatic inspection system of pavement distress images is desired in hope of resolving the above problems.
This paper presents a novel method to classify pavement distress images. The system is called pavement distress recognition system (PDRS), and it is installed on a pavement inspection vehicle with image acquiring devices. First, pavement images were processed to show only black-and-white pixels that can render true pavement cracks. Then, the pavement images were transformed into a set of clusters in order to capture the distress locations of each crack. Next, the distress types, i.e., horizontal, vertical, alligator-like, or man-hole-like, were obtained by applying a decision tree algorithm. Then, the system saves the data and images into a database and provides spatial query functions to users to retrieve crack information. Finally, the pavement distress database is generated to save the information of pavement distress, and using crack grouping algorithm (CGA) and crack linking algorithm (CLA) to create the spatial relation for distress management. The present results showed that our method can successfully recognize various types of pavement distress. Our system also provides information regarding pavement crack lifecycle, i.e., when the crack was identified, when it was fixed, etc., so that public road agencies can define maintenance plans in accordance with real pavement conditions.
關鍵字(中) ★ 路面缺陷檢測
★ 數位影像處理
★ 鋪面管理系統
★ 資料探勘關鍵字(英) ★ Data Mining
★ Digital Image Processing
★ Pavement Management System
★ Pavement Distress Detection論文目次 摘要 ............................................................................................................................................ i
Abstract .................................................................................................................................... ii
Table of Contents ...................................................................................................................... v
List of Figures .......................................................................................................................... ix
List of Tables .......................................................................................................................... xiii
Chapter 1 Introduction ............................................................................................................ 1
1.1 Research Background ................................................................................................... 1
1.2 Motivation and Objectives............................................................................................ 5
1.3 Research Scope ............................................................................................................. 5
1.4 Methodology ................................................................................................................. 7
1.5 Thesis Organization ...................................................................................................... 8
Chapter 2 Literature Review ................................................................................................. 10
2.1 Distress Type Definition ............................................................................................. 10
2.2 Pavement Inspection Vehicle ...................................................................................... 12
2.3 Pavement Image Recognition ..................................................................................... 14
2.3.1 Illumination Restoration .................................................................................. 14
2.3.2 Distress Dilatation ........................................................................................... 15
2.3.3 Image Thresholding ......................................................................................... 15
2.3.4 Distress Extraction ........................................................................................... 17
2.3.5 Distress Classification ..................................................................................... 19
2.4 Spatial Database ......................................................................................................... 22
2.5 Summary ..................................................................................................................... 23
Chapter 3 Pavement Distress Recognition Model ............................................................... 25
3.1 Wafer Image Detection ............................................................................................... 25
3.2 Overview of PDRM .................................................................................................... 29
3.3 Pavement Image Recognition ..................................................................................... 32
3.3.1 Illumination Restoration .................................................................................. 33
3.3.2 Distress Dilatation ........................................................................................... 35
3.3.3 Image Thresholding ......................................................................................... 37
3.3.4 Distress Extraction ........................................................................................... 38
3.3.5 Distress Classification ..................................................................................... 40
3.4 Pavement Image Database .......................................................................................... 42
3.4.1 Image Table Design ......................................................................................... 42
3.4.2 Crack Grouping Algorithm .............................................................................. 45
3.4.3 Crack Linking Algorithm ................................................................................ 47
Chapter 4 Pavement Distress Recognition System .............................................................. 50
4.1 Implementation of PDRS ........................................................................................... 50
4.1.1 Preprocessing ................................................................................................... 50
4.1.2 Crack Extraction .............................................................................................. 53
4.1.3 Crack Classification ......................................................................................... 57
4.1.4 Pavement Image Database ............................................................................... 59
4.2 Validation of PDRS .................................................................................................... 60
4.2.1 Program Interface ............................................................................................ 60
4.2.2 Experiment Results of Low Cracking Road .................................................... 62
4.2.3 Experiment Results of High Cracking Road ................................................... 66
4.2.4 Experiment Results of Distress Cycle ............................................................. 70
4.2.5 Experiment Results of Collapse Distress ........................................................ 74
4.2.6 Results of Failure Recognition ........................................................................ 76
4.3 Other PDR Algorithm and Discussion ....................................................................... 77
Chapter 5 Conclusions and Recommendations ................................................................... 79
5.1 Conclusions ................................................................................................................ 79
5.2 Recommendations ...................................................................................................... 80
5.3 Contributions .............................................................................................................. 81
Reference ................................................................................................................................ 83
Appendix A The Results of PDRS ....................................................................................... 91
A1.1 The results of PDRS for alligator cracking .............................................................. 91
A1.2 The results of PDRS for longitudinal cracking ........................................................ 94
A1.3 The results of PDRS for transverse cracking ........................................................... 98
A1.4 The results of PDRS for manhole cover ................................................................ 101
參考文獻 Åsmund, H. (2001) “Measurement with laser RST on cement concrete.” Swedish National Road and Transport Research Institute (VIT),1-36.
Capuruço, R. A. C., Tighe, S. L., Ningyuan, L. and Kazmierowski, T. (2006) “Performance evaluation of sensor and image-based technologies for automated pavement condition surveys.” Transportation Research Record, 1968, 47-52.
Chang, C. Y., Li, C. H., Chang, J. W. and Jeng, M. D. (2009) “An unsupervised neural network approach for automatic semiconductor wafer defect inspection.” Expert Systems with Applications, 36(1), 950-958.
Chang, C. Y., Li, C. H., Chang, Y. C. and Jeng, M. D. (2009) “Wafer defect inspection by neural analysis of region features.” Journal of Intelligent Manufacturing, 23, 1-12.
Cheng, H. D. and Miyojim, M. (1998) “Novel system for automatic Pavement distress detection.” ASCE Journal of Computing in Civil Engineering, 12(3), 145-152.
Cheng, H. D., Chen, J. R., Glazier, C. and Hu, Y. G. (1999) “Novel approach to pavement cracking detection based on fuzzy set theory.” ASCE Journal of Computing in Civil Engineering, 13(4), 270-280.
Cheng, H. D., Shi, X. J. and Glazier, C. (2003) “Real-time image thresholding based on sample space reduction and interpolation approach.” ASCE Journal of Computing in Civil Engineering, 17(4), 264-272.
Chou, P. B., Rao, A. R., Sturzenbecker, M. C., Wu, F. Y. and Brecher, V. H. (1997) “Automatic defect classification for semiconductor manufacturing.” Machine Vision and Applications, 9(4), 201-214.
Christl, A. (2005) “Introduction to spatial data management with PostGIS.” An Introduction to PostgreSQL and PostGIS, Retrieved May 23, 2010, from http://www.mapbender.org/presentations/Spatial_Data_Management_Arnulf_Christl/Spatial_Data_Management_Arnulf_Christl.pdf
Chua, K. M. and Xu L. (1994) “Simple procedure for identifying pavement distresses from video images.” ASCE Journal of Transportation, 120(3), 412-431.
Fugro Roadware (2010) “ARAN and WiseCrax.” Roadware Report, Retrieved June 3, 2010, from http://www.roadware.com/_lib/pdf/datasheet.wisecrax.pdf
Gonzalez, R. C. and Woods, R. E. (1992) “Digital image processing.” New York: Addison-Wesley Publishing Company.
Groeger, J. L., Stephanos, P., Dorsey, P. and Chapman, M. (2003) “Implementation of automated network level crack detection processes in the state of Maryland.” Transportation Research Board, 109-116.
Huang, C. J. (2007) “Clustered defect detection of high quality chips using self-supervised multilayer perceptron.” Expert Systems with Applications: An International Journal, 33(4), 996-1003.
Huang, Y. and Xu, B. (2005) “An automatic pavement surface distress inspection system.” Journal of ASTM International, 2(10), 1-11.
Huang, Y. and Xu, B. (2006) “Automatic inspection of pavement cracking distress.” Journal of Electronic Imaging, 15(1), 013017–6.
Ikhlas, A. Q., Osama A. and Michael E. K. (2003) “Analysis of edge-detection techniques for crack identification in bridges.” ASCR Journal of Computing in Civil Engineering, 17(4), 255-263.
Iyer, S. and Sinha, S. K.(2005) “A robust approach for automatic detection and segmentation of cracks in underground pipeline images.” Image and Vision Computing, 23(10), 921-933.
Iyer, S. and Sinha, S. K.(2006) “Segmentation of pipe images for crack detection in buried sewers.” Image and Vision Computing, 21(6), 395-410.
Kaseko, M. S., Lo, Z. P. and Ritchie, S. G. (1994) “Comparison of traditional and neural classifiers for pavement-crack detection.” ASEC Journal of Transportation Engineering, 120(4), 552-569.
Koutsopoulos, H. N. and Downey, A. B. (1993) “Primitive-based classification of pavement cracking images.” ASCE Journal of Transportation Engineering, 119(3), 402-418.
Koutsopoulos, H. N., Sanhouri I. E. and Downey A. B. (1993) “Analysis of segmentation algorithms for pavement distress images.” ASCE Journal of Transprotation Engineering, 119(6), 868-888.
Lai, S. H. (2000) “Robust image matching under partial occlusion and spatially varying illumination change.” Computer Vision Image Understand, 78, 84-98.
Lee, B. J. and Lee, H. (2004) “Position-invariant neural network for digital pavement crack analysis.” Computer-Aided Civil and Infrastructure Engineering, 19, 105-118.
Lee, H. and Oshima, H. (1994) “New crack-imaging procedure using spatial autocorrelation function.” ASCE Journal of Transportation Engineering, 120(2), 206-228.
Lin, J. D. and Chen, C. T. (2009) “An automatic pavement roughness measurement and distress image detection system.” Technique Report, NCU.
Lin, J. D. and Chen, C. T. (2010) “Application of line scan CCD Camera on Pavement Distress Image.” Technique Report, NCU.
Lou, Z., Gunaratne, M., Lu, J. J. and Dietrich, B. (2001) “Application of neural network model to forecast short-term pavement crack condition: florida case study.” Journal of Infrastructure Systems, 7(4), 166-171.
MacQueen, J. B. (1967) “Some methods for classifica-tion and analysis of multivariate observations.” Pro-ceedings of the Fifth Symposium on Math, Statistics, 281-297, C.A.
Meignen, D., Bernadet, M. and Briand, H. (1997) "One application of neural networks for detection of defects using video data bases: identification of road distresses." 8th International Workshop on Database and Expert Systems Applications, 459-464.
Monti, M. (1995) “Large-area laser scanner with holographic detector optics for real-time recognition of cracks in road surfaces.” Optical Engineering, 34(7), 2017-2023.
Nazef, A., Mraz, A., Gunaratne, M. and Choubane, B.(2006) “Experimental evaluation of a pavement imaging system: Florida Department of Transportation’s multipurpose survey vehicle.” Transportation Research Record, 1974, 97-106.
Newman, T. S. and Jain, A. K. (1995) “A survey of automatic visual inspection.” Computer Vision Image Understand, 61, 231-262.
Puan, O. C., Mustaffar, M. and Ling, T. C. (2007) “Automated pavement imagein program (APIP) for pavement cracks classification and quantification.” Malaysian Journal of Civil Engineering, 19(1), 1-16.
Hawks, N. E. and Teng, T. P. (1993) ”Distress identification manual for the long-term pavement performance project.” Strategic Highway Research Program, SHRP-P-338, National Research Council, D.C.
Subirats, P., Dumoulin J., Legeay, V. and Barba, D. (2006) “Automation of pavement surface crack detection using the continuous wavelet transform.” IEEE International Conference on Image Processing (ICIP), 3037-3040, Atlanta.
Tobin, K. W. (1999) “Inspection in semiconductor manufacturing.” Webster’s Encyclopedia of Electrical and Electronic Engineering, 10, 242-262.
Tsai, Y. C., Kaul, V. and Mersereau, R. M. (2010) “Critical assessment of pavement distress segmentation methods.” ASCE Journal of Transportation Engineering, 136(1), 11-19.
Wang, C. H. (2008) “Recognition of semiconductor defect patterns using spatial filtering and spectral clustering.” Expert Systems with Applications, 34(3), 1914-1923.
Wang, C. H., Kuo, W. and Bensmail H. (2006) “Detection and classification of defect patterns on semiconductor wafers.” IIE Transactions, 38(12), 1059-1068.
Wang, C. J., Wu, C. F. and Wang, C. C. (2002) “Image processing techniques for wafer defect cluster identification.” IEEE Design & Test of Computers, 19(2), 44-48.
Wang, C. P. (2000) “Designs and implementations of automated systems for pavement surface distress survey.” Journal of Infrastructure Systems, 6(1), 24-32.
Wang, C. P. and Gong, W. (2005) “Real-time automated survey system of pavement cracking in parallel environment.” Journal of Infrastructure Systems, 11(3), 154-164.
Wang, C. P., Gong, W., Li, X., Elliott, R. P. and Daleiden, J.(2002)“Data analysis of real-time system for automated distress survey.” Transportation Research Record, 1806, 101-109.
Wang, C. P., Li, Q. and Gong, W. (2008) “Wavelet-based pavement distress image edge detection with à trous algorithm.” Transportation Research Record, 2024, 73-81.
Wang, G., Xu, X. W., Xian, L. and He, A. Z. (2008) “Algorithm based on the finite ridgelet transform for enhancing faint pavement cracks.” Optical Engineering, 47(1),1-10.
Wang, G., Zhang, Z. F., Huang, Y. J. and Zhao, Y. L. (2007) “An improved multifractal method for pavement cracks extractionl.” Engineering Computations: International Journal for Computer-Aided Engineering and Software, 24(7), 712-722.
Wu, J. and Tsai, Y. (2006) "Enhanced roadway inventory using 2-D sign video image recognition algorithm." Journal of Computer-Aided Civil and Infrastructure Engineering, 21(5), 369-382.
Yang, C., Tsai, Y. and Wang Z. (2009) "Algorithm for spatial clustering of pavement segments." Journal of Computer-Aided Civil and Infrastructure Engineering, 24(2), 93-108.
Zheng, J., Wang, Y., Nihan, N. L. and Hallenbeck, M. E. (2006) “Extracting roadway background image: mode-based approach.” Transportation Research Record, 1944, 82-88.
Zhou, J., Huang, P. S. and Chiang, F. P. (2005) “Wavelet-based pavement distress classification.” Transportation Research Record, 1940, 89-98.
Zhou, J., Huang, P. S. and Chiang, F. P. (2006) “Wavelet-based pavement distress detection and evaluation.” Optical Engineering, 45(2), 1-10.
指導教授 周建成(Chien-Cheng Chou) 審核日期 2010-7-27 推文 plurk
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