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Visual-based crack detection and skeleton extraction of cement surface

Research output: Chapter in Book/Report/Conference proceedingConference contribution

In order to realize the design of vision-based cement crack repair robot, it is necessary to accurately recognize and extract features of cracks. In this paper, three kinds of typical crack are selected to study, which are fine crack, reticulated crack and dark crack. Firstly, image filtering and image enhancement are used to pre-process the collected image to reduce the influence of noise on detection and enhance the contrast between image background and crack area. Then, the multi-scale morphological operation is applied to extract the fracture edge features effectively. The experimental results show that the proposed edge regions are obviously different from the background regions. Furthermore, by calculating and selecting the area of the largest connected area, the noise can be eliminated to the greatest extent. Finally, the traditional skeleton extraction algorithm is improved to eliminate the number of burrs in the traditional skeleton algorithm. By remapping the cracks images to color images, it can be found that the crack recognition and skeleton extraction meet the requirements, which can provide corresponding technical support for the navigation design of the crack repair robot.
Original languageEnglish
Title of host publicationIntelligent Robotics and Applications
Subtitle of host publication12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part V
EditorsHaibin Yu, Jinguo Liu, Lianquing Liu, Zhaojie Ju, Yuwang Liu, Dalin Zhou
PublisherSpringer
Chapter44
Pages541-552
Number of pages12
ISBN (Electronic)978-3-030-27541-9
ISBN (Print)978-3-030-27540-2
DOIs
Publication statusPublished - 6 Aug 2019
Event12th International Conference on Intelligent Robotics and Applications - Shenyang, China
Duration: 8 Aug 201911 Aug 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11744
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
PublisherSpringer
Volume11744
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Intelligent Robotics and Applications
Abbreviated titleICIRA 2019
CountryChina
CityShenyang
Period8/08/1911/08/19

Documents

  • Jiang_Visual-based Crack Detection_ICIRA2019_425_final_v3

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in Intelligent Robotics and Applications. ICIRA 2019. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-27541-9_44.

    Accepted author manuscript (Post-print), 1.27 MB, PDF document

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