Computer Vision Implementation in Scratch Inspection and Color Detection on The Car Roof Surface
##plugins.themes.academic_pro.article.sidebar##
Download : 357
##plugins.themes.academic_pro.article.main##
Abstract
The automotive industry in Indonesia is one of the important pillars in the manufacturing sector. High speed productivity and high-level quality of their products are required for increasing the company value. The car roof inspection is still in low speed and inadequate quality control method for most of the car company’s manufacturing production line. This inspection process is highly dependent to human expertise and skill of the assembly operators. This study purposes to increase the productivity in the inspection line by eliminate human error using computer vision. This research aims to find an automatic and accurate method for visual inspection of the color quality and scratch defect on car roof surfaces, using the data generated from the standardized database QR Code. The proposed solution uses MobileNet-SSD method of computer vision in recognizing scratch defects, while color detection of the roof is attained through hue saturation method. The study employs data capture from a camera and compares the amount of object inspected in real time. The scratch inspection with 70 image samples and 4000 training steps results in a 3.33% error with color detection and inspection success rate of 80%.
##plugins.themes.academic_pro.article.details##
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c): Wahyu Adhie Candra, Adhitya Sumardi Sunarya, Wening Sukma Saraswati (2023)References
[2] A. Muñoz, X. Mahiques, J. E. Solanes, A. Martí, L. Gracia, and J. Tornero, “Mixed reality-based user interface for quality control inspection of car body surfaces,” J Manuf Syst, vol. 53, pp. 75–92, Oct. 2019, doi: 10.1016/J.JMSY.2019.08.004.
[3] A. Waluya, … M. I.-I. J. of, and undefined 2019, “How product quality, brand image, and customer satisfaction affect the purchase decisions of Indonesian automotive customers,” inderscienceonline.com, vol. 10, no. 2, pp. 177–193, 2019, doi: 10.1504/IJSEM.2019.100944.
[4] P. Anggraeni, W. A. Candra, M. Defoort, and M. Djemai, “Experimental implementation of fixed-time leader-follower axial alignment tracking,” in 2019 International Conference on Mechatronics, Robotics and Systems Engineering (MoRSE), 2019, pp. 86–91.
[5] J. Inovasi Vokasional dan Teknologi, P. Anggraeni, and dan MTA Asshydiqi, “Penerapan Algoritma ORB SLAM-2 Pada Sistem Pemetaan Lingkungan Multi Robot,” INVOTEK: Jurnal Inovasi Vokasional dan Teknologi, vol. 20, no. 3, pp. 123–134, Oct. 2020, doi: 10.24036/INVOTEK.V20I3.854.
[6] A. Chouchene, A. Carvalho, T. M. Lima, F. Charrua-Santos, G. J. Osório, and W. Barhoumi, “Artificial Intelligence for Product Quality Inspection toward Smart Industries: Quality Control of Vehicle Non-Conformities,” ICITM 2020 - 2020 9th International Conference on Industrial Technology and Management, pp. 127–131, Feb. 2020, doi: 10.1109/ICITM48982.2020.9080396.
[7] A. Younis, L. Shixin, J. N. Shelembi, and Z. Hai, “Real-time object detection using pre-trained deep learning models mobilenet- SSD,” ACM International Conference Proceeding Series, pp. 44–48, Jan. 2020, doi: 10.1145/3379247.3379264.
[8] C. Döring, A. Eichhorn, X. Wang, and R. Kruse, “Improved classification of surface defects for quality control of car body panels,” IEEE International Conference on Fuzzy Systems, pp. 1476–1481, 2006, doi: 10.1109/FUZZY.2006.1681903.
[9] Q. Zhou, R. Chen, B. Huang, C. Liu, J. Yu, and X. Yu, “An Automatic Surface Defect Inspection System for Automobiles Using Machine Vision Methods,” Sensors 2019, Vol. 19, Page 644, vol. 19, no. 3, p. 644, Feb. 2019, doi: 10.3390/S19030644.
[10] W. Liu, D. Anguelov, D. Erhan, … C. S.-C. V., and undefined 2016, “Ssd: Single shot multibox detector,” Springer, Accessed: Mar. 07, 2023. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-319-46448-0_2
[11] Y. Li, H. Huang, Q. Xie, L. Yao, and Q. Chen, “Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD,” Applied Sciences 2018, Vol. 8, Page 1678, vol. 8, no. 9, p. 1678, Sep. 2018, doi: 10.3390/APP8091678.
[12] N. Mohd Ali, N. Khair Alang Md Rashid, and Y. Mohd Mustafah, “Performance comparison between RGB and HSV color segmentations for road signs detection,” Trans Tech Publ, 2013, doi: 10.4028/www.scientific.net/AMM.393.550.
[13] H.-C. Kang et al., “HSV color-space-based automated object localization for robot grasping without prior knowledge,” mdpi.com, 2021, doi: 10.3390/app11167593.
[14] J. Tang, X. Peng, X. Chen, and B. Luo, “An Improved Mobilenet-SSD Approach for Face Detection,” Chinese Control Conference, CCC, vol. 2021-July, pp. 8072–8076, Jul. 2021, doi: 10.23919/CCC52363.2021.9549245.
[15] L. Arnal, J. E. Solanes, J. Molina, and J. Tornero, “Detecting dings and dents on specular car body surfaces based on optical flow,” J Manuf Syst, vol. 45, pp. 306–321, Oct. 2017, doi: 10.1016/J.JMSY.2017.07.006.
[16] X. Wei, A. Manori, N. Devnath, … N. P.-I. J. of, and undefined 2017, “QR code based smart attendance system,” academia.edu, Accessed: Mar. 26, 2023. [Online]. Available: https://www.academia.edu/download/56635087/1.pdf
[17] A. Espejel-Trujillo, … I. C.-C.-P., and undefined 2012, “Identity document authentication based on VSS and QR codes,” Elsevier, Accessed: Mar. 26, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2212017312002551