A novel automatic dam crack detection algorithm based on local-global clustering
Dam crack detection is necessary to ensure the safety of dams. However, traditional detection methods always perform poorly, with a low detection rate and high false alarm rate, due to the complex underwater environment. In this paper, a novel automatic dam crack detection algorithm (CrackLG) is pro...
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| Published in | Multimedia tools and applications Vol. 77; no. 20; pp. 26581 - 26599 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.10.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-018-5880-1 |
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| Abstract | Dam crack detection is necessary to ensure the safety of dams. However, traditional detection methods always perform poorly, with a low detection rate and high false alarm rate, due to the complex underwater environment. In this paper, a novel automatic dam crack detection algorithm (CrackLG) is proposed based on local-global clustering analysis that can find cracks on dam surfaces accurately and quickly using images as well as reduce human subjectivity. First, an image shot of an underwater dam surface is divided into non-overlapping image blocks after pre-processing. Then, image blocks containing crack pixels are identified by local clustering analysis. Second, the image is binarized by adaptive bi-level thresholding based on the local gray intensity. Meanwhile, some noise is removed based on the computed optimal threshold. After extracting global 3-D features, final crack regions are obtained by global clustering analysis. The advantage of CrackLG is that the threshold for realizing image binarization is self-adaptive. Additionally, it can automatically perform crack detection without human supervision. The simulation and comparison show that the proposed CrackLG method is more effective for underwater dam crack detection. |
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| AbstractList | Dam crack detection is necessary to ensure the safety of dams. However, traditional detection methods always perform poorly, with a low detection rate and high false alarm rate, due to the complex underwater environment. In this paper, a novel automatic dam crack detection algorithm (CrackLG) is proposed based on local-global clustering analysis that can find cracks on dam surfaces accurately and quickly using images as well as reduce human subjectivity. First, an image shot of an underwater dam surface is divided into non-overlapping image blocks after pre-processing. Then, image blocks containing crack pixels are identified by local clustering analysis. Second, the image is binarized by adaptive bi-level thresholding based on the local gray intensity. Meanwhile, some noise is removed based on the computed optimal threshold. After extracting global 3-D features, final crack regions are obtained by global clustering analysis. The advantage of CrackLG is that the threshold for realizing image binarization is self-adaptive. Additionally, it can automatically perform crack detection without human supervision. The simulation and comparison show that the proposed CrackLG method is more effective for underwater dam crack detection. |
| Author | Fan, Xinnan Xie, Yingjuan Wu, Jingjing Zhang, Xuewu Shi, Pengfei |
| Author_xml | – sequence: 1 givenname: Xinnan surname: Fan fullname: Fan, Xinnan organization: College of Internet of Things Engineering, Hohai University – sequence: 2 givenname: Jingjing surname: Wu fullname: Wu, Jingjing organization: College of Internet of Things Engineering, Hohai University – sequence: 3 givenname: Pengfei surname: Shi fullname: Shi, Pengfei email: shipf@hhu.edu.cn organization: College of Internet of Things Engineering, Hohai University – sequence: 4 givenname: Xuewu surname: Zhang fullname: Zhang, Xuewu organization: College of Internet of Things Engineering, Hohai University – sequence: 5 givenname: Yingjuan surname: Xie fullname: Xie, Yingjuan organization: College of Internet of Things Engineering, Hohai University |
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| Cites_doi | 10.1109/TITS.2012.2208630 10.1109/TIT.2013.2255021 10.12989/cac.2012.10.3.277 10.1016/j.compeleceng.2016.08.008 10.1007/s00027-014-0377-0 10.1109/TSMC.1979.4310076 10.12989/acc2013.1.3.227 10.1016/j.csda.2006.12.012 10.1016/j.autcon.2013.06.011 10.1007/s11431-008-6012-3 10.1109/TIM.2015.2509278 10.1111/j.1467-8667.2011.00716.x 10.1080/15732479.2011.593891 10.1109/TITS.2015.2505518 10.1002/eqe.454 10.1109/ICASI.2017.7988574 10.1109/ICIST.2016.7483459 10.1109/ICIS.2015.7166612 10.1109/TITS.2015.2477675 10.1109/ICICIP.2012.6391474 10.1109/OCEANSAP.2016.7485370 10.1109/CADIAG.2017.8075714 10.1177/1475921716651039 10.1117/12.805437 10.23919/EUSIPCO.2017.8081565 |
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| Keywords | Dam Feature extraction Crack detection CrackLG K-means clustering Threshold |
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| SubjectTerms | Cluster analysis Clustering Computer Communication Networks Computer Science Computer simulation Control charts Dam safety Data Structures and Information Theory False alarms Feature extraction Human performance Image detection Multimedia Information Systems Noise intensity Special Purpose and Application-Based Systems Underwater |
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| Title | A novel automatic dam crack detection algorithm based on local-global clustering |
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