Browse > Article
http://dx.doi.org/10.3745/KTSDE.2022.11.5.211

A Study on Tire Surface Defect Detection Method Using Depth Image  

Kim, Hyun Suk (한국산업기술대학교 스마트팩토리융합과)
Ko, Dong Beom (한국전자통신연구원 차세대시스템SW)
Lee, Won Gok (한국산업기술대학교 인공지능기술사업화연구소)
Bae, You Suk (한국산업기술대학교 컴퓨터공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.5, 2022 , pp. 211-220 More about this Journal
Abstract
Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.
Keywords
Tire Defect Detection; Depth Image; Deep Learning; Computer Vision; Image Processing;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Automation Technology, 3D Laser Sensors for Measurements by Means of Laser-Triangulation [Internet], https://www.automationtechnology.de/cms/en/3d-laser-sensors-for-measurements-by-means-of-laser-triangulation.
2 H. J. Park, Y. W. Lee and B. G. Kim, "Efficient tire wear and defect detection algorithm based on deep learnin," Journal of Korea Multimedia Society, Vol.24, No.8, pp.1026-1034, Aug. 2021.   DOI
3 R. Wang, Q. Guo, S. Lu, and C. Zhang, "Tire defect detection using fully convolutional network," IEEE Access, Vol.7, pp.43502-43510, Jan. 2019.   DOI
4 J. Redmon and A. Farhadi "YOLO9000: Better, faster, stronger," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.7263-7271, 2017.
5 Ministry of Culture, Sports and Tourism, Smart Factory (Intelligent Factory) [Internet], https://www.korea.kr/special/policyCurationView.do?newsId=148866604.
6 Y. J. Cho, "The strategy for Smart Factory of Korea in the era of the Industry 4.0," Communications of the Korean Institute of Information Scientists and Engineers, Vol.35, No.6, pp.40-48, Jun. 2017.
7 K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask R-CNN," in Proceedings of the IEEE International Conference on Computer Vision, pp.2961-2969, 2017.
8 P. Wang, P. Chen, Y. Yuan, D. Liu, Z. Huang, X. Hou, and G. Cottrell, "Understanding convolution for semantic segmentation," in Proceeding of the IEEE Winter Conference on Applications of Computer Vision, Jun. 2018.
9 J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition, Mar. 2015.
10 H. Y. Chan, F. K. Lam, and H. Zhu, "Adaptive thresholding by variational method," IEEE Transactions on Image Processing, Vol.7, No.3, Mar. 1998.
11 P. Roy, G. Dey, S. Dutta, S. Chakraborty, N. Dey, and R. Ray, "Adaptive thresholding: A comparative study," in Proceeding of the International Conference on Control, Instrumentation, Communication and Computational Technologies, Jul. 2014.
12 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.779-788, 2016.
13 I. S. Oh, "Binary image," in Computer Vision, 3rd ed, Hanbit Academy.Inc, pp.68-71, 2018.
14 Y. H. Lee and Y. S. Kim, "Comparison of CNN and YOLO for object detection," Journal of the Semiconductor & Display Technology, Vol.19, No.1. Mar. 2020.
15 J. Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, Apr. 2018.
16 N. S. Punn, S. K. Sonbhadra, S. Agarwal, and G. Rai, "Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques," arXiv preprint arXiv:2005.01385, Apr. 2021.