Visual-based crack detection and skeleton extraction of cement surface

Du Jiang; Gongfa Li; Ying Sun; Jianyi Kong; Bo Tao; Dalin Zhou; Disi Chen; Zhaojie Ju

doi:10.1007/978-3-030-27541-9_44

Visual-based crack detection and skeleton extraction of cement surface

Du Jiang
, Gongfa Li
, Ying Sun
, Jianyi Kong
, Bo Tao
, Dalin Zhou
, Disi Chen
, Zhaojie Ju

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

414 Downloads (Pure)

Abstract

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 language	English
Title of host publication	Intelligent Robotics and Applications
Subtitle of host publication	12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part V
Editors	Haibin Yu, Jinguo Liu, Lianquing Liu, Zhaojie Ju, Yuwang Liu, Dalin Zhou
Publisher	Springer
Chapter	44
Pages	541-552
Number of pages	12
ISBN (Electronic)	978-3-030-27541-9
ISBN (Print)	978-3-030-27540-2
DOIs	https://doi.org/10.1007/978-3-030-27541-9_44
Publication status	Published - 6 Aug 2019
Event	12th International Conference on Intelligent Robotics and Applications - Shenyang, China Duration: 8 Aug 2019 → 11 Aug 2019

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11744
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	11744
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Intelligent Robotics and Applications
Abbreviated title	ICIRA 2019
Country/Territory	China
City	Shenyang
Period	8/08/19 → 11/08/19

Keywords

crack recognization
multi-scale morphology
skeletonization

Access to Document

10.1007/978-3-030-27541-9_44

Jiang_Visual-based Crack Detection_ICIRA2019_425_final_v3
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

Cite this

Jiang, D., Li, G., Sun, Y., Kong, J., Tao, B., Zhou, D., Chen, D., & Ju, Z. (2019). Visual-based crack detection and skeleton extraction of cement surface. In H. Yu, J. Liu, L. Liu, Z. Ju, Y. Liu, & D. Zhou (Eds.), Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part V (pp. 541-552). (Lecture Notes in Computer Science; Vol. 11744), (Lecture Notes in Artificial Intelligence; Vol. 11744). Springer. https://doi.org/10.1007/978-3-030-27541-9_44

Jiang, Du ; Li, Gongfa ; Sun, Ying et al. / Visual-based crack detection and skeleton extraction of cement surface. Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part V. editor / Haibin Yu ; Jinguo Liu ; Lianquing Liu ; Zhaojie Ju ; Yuwang Liu ; Dalin Zhou. Springer, 2019. pp. 541-552 (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence).

@inproceedings{df09e4ba4bdd4215afc107c2b49b5bf5,

title = "Visual-based crack detection and skeleton extraction of cement surface",

abstract = "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.",

keywords = "crack recognization, multi-scale morphology, skeletonization",

author = "Du Jiang and Gongfa Li and Ying Sun and Jianyi Kong and Bo Tao and Dalin Zhou and Disi Chen and Zhaojie Ju",

year = "2019",

month = aug,

day = "6",

doi = "10.1007/978-3-030-27541-9\_44",

language = "English",

isbn = "978-3-030-27540-2",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "541--552",

editor = "Haibin Yu and Jinguo Liu and Lianquing Liu and Zhaojie Ju and Yuwang Liu and Dalin Zhou",

booktitle = "Intelligent Robotics and Applications",

address = "Netherlands",

note = "12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 ; Conference date: 08-08-2019 Through 11-08-2019",

}

Jiang, D, Li, G, Sun, Y, Kong, J, Tao, B, Zhou, D, Chen, D & Ju, Z 2019, Visual-based crack detection and skeleton extraction of cement surface. in H Yu, J Liu, L Liu, Z Ju, Y Liu & D Zhou (eds), Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part V. Lecture Notes in Computer Science, vol. 11744, Lecture Notes in Artificial Intelligence, vol. 11744, Springer, pp. 541-552, 12th International Conference on Intelligent Robotics and Applications, Shenyang, China, 8/08/19. https://doi.org/10.1007/978-3-030-27541-9_44

Visual-based crack detection and skeleton extraction of cement surface. / Jiang, Du; Li, Gongfa; Sun, Ying et al.
Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part V. ed. / Haibin Yu; Jinguo Liu; Lianquing Liu; Zhaojie Ju; Yuwang Liu; Dalin Zhou. Springer, 2019. p. 541-552 (Lecture Notes in Computer Science; Vol. 11744), (Lecture Notes in Artificial Intelligence; Vol. 11744).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Visual-based crack detection and skeleton extraction of cement surface

AU - Jiang, Du

AU - Li, Gongfa

AU - Sun, Ying

AU - Kong, Jianyi

AU - Tao, Bo

AU - Zhou, Dalin

AU - Chen, Disi

AU - Ju, Zhaojie

PY - 2019/8/6

Y1 - 2019/8/6

N2 - 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.

AB - 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.

KW - crack recognization

KW - multi-scale morphology

KW - skeletonization

U2 - 10.1007/978-3-030-27541-9_44

DO - 10.1007/978-3-030-27541-9_44

M3 - Conference contribution

SN - 978-3-030-27540-2

T3 - Lecture Notes in Computer Science

SP - 541

EP - 552

BT - Intelligent Robotics and Applications

A2 - Yu, Haibin

A2 - Liu, Jinguo

A2 - Liu, Lianquing

A2 - Ju, Zhaojie

A2 - Liu, Yuwang

A2 - Zhou, Dalin

PB - Springer

T2 - 12th International Conference on Intelligent Robotics and Applications

Y2 - 8 August 2019 through 11 August 2019

ER -

Jiang D, Li G, Sun Y, Kong J, Tao B, Zhou D et al. Visual-based crack detection and skeleton extraction of cement surface. In Yu H, Liu J, Liu L, Ju Z, Liu Y, Zhou D, editors, Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Shenyang, China, August 8–11, 2019, Proceedings, Part V. Springer. 2019. p. 541-552. (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-030-27541-9_44

Visual-based crack detection and skeleton extraction of cement surface

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Keywords

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