Paper Info
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.
Year
DOI
Venue
2019
10.1007/978-3-030-27541-9_44
INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT V
Keywords
DocType
Volume
Crack recognization, Multi-scale morphology, Skeletonization
Conference
11744
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Du Jiang19714.40
Gongfa Li223943.45
Ying Sun329140.03
Jian-yi Kong4113.65
Bo Tao560.74
Dalin Zhou6168.09
Disi Chen7397.70
Zhaojie Ju828448.23