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研究生:陳世植
研究生(外文):Shi-Zhi Chen
論文名稱:以邊緣偵測為基礎之影像分割技術於汙水管線缺失形態萃取
論文名稱(外文):Extraction of Sewer Pipe defects using Morphological segmentation based on Edge Detection
指導教授:蘇東青蘇東青引用關係
指導教授(外文):Tong-Ching Su
口試委員:楊明德劉霈洪集輝
口試委員(外文):Ming-Der YangPei LuJi-Hwei Horng
口試日期:2012-06-21
學位類別:碩士
校院名稱:國立金門大學
系所名稱:土木與工程管理學系碩士班
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:2012
畢業學年度:100
語文別:中文
論文頁數:94
中文關鍵詞:污水管線缺失CCTV影像影像分割斷開-帽頂轉換MSED
外文關鍵詞:Sewer pipe defectCCTV imagesImage segmentationOpening top-hat operationMSED
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目前,影像處理及人工智慧技術已應用於發展自動化汙水管線缺失診斷系統,輔助檢視人員從closed circuit television (CCTV)影像進行管線結構狀況診斷,並減低傳統人為診斷方式可能產生之誤診或費時等缺點。現有已發展之自動化汙水管線缺失診斷系統,大多依據管線缺失的形態特徵進行診斷,常用的影像分割方法為斷開帽頂轉換(opening top-hat operation)配合Otsu影像二元化技術進行管線缺失形態萃取。除了斷開帽頂轉換外,本研究亦提出morphological segmentation based on edge detection (MSED)演算法,先對CCTV檢視影像進行邊緣檢測,依據邊緣檢測結果進行管線缺失形態萃取。本研究以英國水研究中心範例影像施行斷開帽頂轉換及MSED演算法,進行兩種管線缺失形態萃取比較。進行管線缺失形態萃取前,設計3×3及5×5兩種不同尺寸之中值濾波器對影像進行濾波處理,並比較其濾波成效對管線缺失形態萃取之影響。實驗結果發現斷開帽頂轉換採5×5中值濾波器僅可對接頭脫開(open joint)萃取出較適形態,MSED採3×3中值濾波器則適於破裂(fracture)、剝落(spalling)、變形(deformation)、孔洞(hole)、崩塌(collapse)等多樣管線缺失類別之形態萃取,故本研究證明所提出的MSED演算法可有效進行管線缺失形態萃取。之後以16幅台中市CCTV檢視影像進行測試,其中8幅顯示破裂(fracture),另8幅顯示接頭脫開(open joint)。測試結果發現斷開帽頂轉換及MSED均受CCTV自走車所提供光源強度與對比影響,而導致無法萃取適當管線缺失形態。為提升自動化汙水管線缺失診斷系統之效能,本研究建議在管線CCTV檢視階段,應建立良好取像條件。
At present, image processing and artificial intelligence techniques have been used to develop diagnostic systems to assist engineers in interpreting sewer pipe defects on CCTV images to overcome human’s fatigue and subjectivity, and time-consumption. Based on the segmented morphologies on images, the diagnostic systems were proposed to diagnose sewer pipe defects. Opening top-hat operation coupled with Otsu’s thresholding is usually applied to morphology extraction. In this thesis, a novel approach of morphological segmentation based on edge detection (MSED) was also presented and applied to identify the morphology representatives for the sewer pipe defects on CCTV images. Before the implementations of opening top-hat operation or MSED, the median filters of 3×3 or 5×5 are employed to reduce image noise as well as keep informative textures. The 8 illustrations available at the Sewerage Rehabilitation Manual of Water Research Centre, UK were selected to be the testing images. Compared with the performances of opening top-hat operation and MSED, median filtering of 5×5 followed by opening top-hat operation is merely suitable in morphology extraction of open joint. However, median filtering of 3×3 followed by MSED could effectively extract the representative morphologies of fracture, spalling, deformation, hole, and collapse. This result demonstrates that MSED outperform opening top-hat operation. Besides, another 16 inspection images showing the sewer pipe defects of fracture and open joint were selected to be tested. The testing result indicates that the representative morphologies could not be extracted due to the inappropriate luminance or image contrast of the CCTV equipment. Hence, a well imaging condition should be built during CCTV inspection inside sewer pipes.
目錄
摘要 ........................................................................................................................................... I
ABSTRACT ............................................................................................................................. II
致謝 ........................................................................................................................................ III
目錄 ......................................................................................................................................... IV
表目錄 ..................................................................................................................................... VI
圖目錄 ................................................................................................................................... VII
第一章 緒論 .............................................................................................................................. 1
1.1 研究背景 ...................................................................................................................... 1
1.1 研究目的 ...................................................................................................................... 2
第二章 文獻探討 ...................................................................................................................... 5
2.1 雜訊濾波 ..................................................................................................................... 5
2.2 特徵萃取 ..................................................................................................................... 6
2.1.1 紋理特徵 .......................................................................................................... 6
2.1.2 形態特徵 .......................................................................................................... 8
2.3 小結 ........................................................................................................................... 10
第二章 文獻探討 .................................................................................................................... 11
3.1 研究方法流程 ........................................................................................................... 11
3.2 中值濾波器 ............................................................................................................... 16
3.3 邊緣偵測 ................................................................................................................... 16
3.3.1 Sobel ................................................................................................................ 18
3.4 數學形態學 ............................................................................................................... 19
3.4.1 形態膨脹 ........................................................................................................ 19
3.4.2 形態侵蝕 ........................................................................................................ 20
3.4.3 形態閉合 ........................................................................................................ 21
3.4.4 形態斷開 ........................................................................................................ 22
3.4.5 斷開-頂帽轉換 ............................................................................................... 23
3.5 Otsu’s 法影像二值化 ................................................................................................. 23
3.6 Morphological Segmentation based on Edge Detection............................................. 26
3.6.1 MSED 演算法進行影像區域生長(0°方向) ................................................... 26
3.6.2 MSED 演算法進行影像區域生長(90°方向) ................................................. 28
3.6.3 MSED 演算法進行影像區域生長(45°方向) ................................................. 30
3.6.4 MSED 演算法進行影像區域生長(135°方向) ............................................... 32
第四章 研究成果與討論 ........................................................................................................ 34
4.1 Water Research Center 範例影像測試 ...................................................................... 34
4.1.1 中值濾波處理 ................................................................................................ 34
4.1.2 斷開-頂帽轉換分割成果 ............................................................................... 36
4.1.3 MSED 演算法分割成果 ................................................................................. 40
4.1.4 比較形態分割成果 ......................................................................................... 46
4.1.5 初步成果討論 ................................................................................................ 50
4.2 台中市下水道CCTV 影像測試 .............................................................................. 51
4.2.1 雜訊濾波處理成果 ........................................................................................ 51
4.2.2 MSED 演算法分割成果 ................................................................................. 54
4.2.3 斷開-頂帽轉換分割成果 ............................................................................... 60
第五章 結論與建議 ................................................................................................................ 66
5.1 結論 ........................................................................................................................... 66
5.2 建議 ........................................................................................................................... 67
參考文獻 ................................................................................................................................. 68
附錄圖 ..................................................................................................................................... 71
附錄一 .............................................................................................................................. 91
附錄二 .............................................................................................................................. 92
附錄三 .............................................................................................................................. 93
參考文獻
英文文獻
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Yang, M. D., & Su, T. C. (2008). Automated diagnosis of sewer pipe defects based on machine learning approaches. Expert Systems with Applications, 35, 1327-1337.

Yang, M. D., & Su, T. C. (2009). Segmenting ideal morphologies of sewer pipe defects on CCTV images for automated diagnosis. Expert Systems with Applications, 36, 3562-3573.

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Yang, M. D., & Su, T. C. (2011). Morphological segmentation based on edge detection for sewer pipe defects on CCTV images. Expert Systems with Applications, 38, 13094-13114.


參考文獻
中文文獻
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陳克智(2011),照相手機的車牌偵測與辨識,國立中央大學資訊工程研究所,碩士論文。

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鄭文瑋(2005),在次像素精準度下的邊緣偵測演算法及其應用,銘傳大學資訊傳播工程學系碩士班,碩士論文。

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