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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, F.C., Jahanshahi, M.R.: NB-CNN: deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion. IEEE Trans. Ind. Electron. 65(5), 4392–4400 (2018)
Yoo, H.-S., Kim, Y.-S.: Development of a crack recognition algorithm from non-routed pavement images using artificial neural network and binary logistic regression. KSCE J. Civ. Eng. 20(4), 1151–1162 (2016)
Shu, Z., Guo, Y.: Algorithm on contourlet domain in detection of road cracks for pavement images. J. Algorithms Comput. Technol. 7(1), 15–26 (2013)
Petrik, M.: Climber-Inspired fuzzy logic approach to crack localization using image analysis. J. Adv. Concr. Technol. 13(2), 103–111 (2015)
Jiang, D., Zheng, Z., Li, G., et al.: Gesture recognition based on binocular vision. Cluster Comput. (2018). https://doi.org/10.1007/s10586-018-1844-5
Li, G., Wu, H., Jiang, G., et al.: Dynamic gesture recognition in the Internet of Things. IEEE Access 7, 23713–23724 (2019)
Nam, Y., Choi, D.Y., Song, B.C.: Power—constrained contrast enhancement algorithm using muhiscale retinex for OLED display. IIEEE Trans. Mage Process. 23(8), 3308–3320 (2014)
Li, G., Jiang, D., Zhou, Y., et al.: Human lesion detection method based on image information and brain signal. IEEE Access 7, 11533–11542 (2019)
Silverberg, J., Yin, P.: Morphology of embryonic epidermis: an empirical multiscale biophysics approach. Biophys. J. 114(3), 205a (2018)
Li, H., Song, D., Liu, Y., et al.: Automatic pavement crack detection by multi-scale image fusion. IEEE Trans. Intell. Transp. Syst. 20(6), 2025–2036 (2018)
Fei, Y., Wang, K.C.P., Zhang, A., et al.: Pixel-level cracking detection on 3D Asphalt pavement images through deep-learning-based CrackNet-V. IEEE Trans. Intell. Transp. Syst. PP(99), 1–12 (2019)
Jiang, D., Li, G., Sun, Y., et al.: Gesture recognition based on skeletonization algorithm and CNN with ASL database. Multimedia Tools Appl. (2018). https://doi.org/10.1007/s11042-018-6748-0
Acknowledgment
This work was supported by grants of National Natural Science Foundation of China This work was supported by grants of National Natural Science Foundation of China (Grant Nos. 51575407, 515505349, 51575338, 51575412, 61733011), the Grants of National Defense Pre-Research Foundation of Wuhan University of Science and Technology (GF201705) and the Open Fund of the Key Laboratory for Metallurgical Equipment and Control of Ministry of Education in Wuhan University of Science and Technology (2018B07) and the DREAM project of EU FP7-ICT (grant no. 611391).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jiang, D. et al. (2019). Visual-Based Crack Detection and Skeleton Extraction of Cement Surface. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11744. Springer, Cham. https://doi.org/10.1007/978-3-030-27541-9_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-27541-9_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27540-2
Online ISBN: 978-3-030-27541-9
eBook Packages: Computer ScienceComputer Science (R0)