Abstract
As a popular technology in the field of artificial intelligence, computer vision is gradually adapting to the needs of convenience for human beings, improving production efficiency and reducing production costs. Therefore, this study proposes a computer vision algorithm to locate and identify the location of defects. For the traditional edge detection algorithm Sobel, LoG, Canny, the decisive factor for the detection effect of paint defect image is the adjustment of parameters, which can’t achieve an adaptive edge detection algorithm for paint defects, so it is thought that the evolution idea of ant colony algorithm can be used to achieve accurate detection of defects. This paper proposes an automatic detection method for vehicle body paint film defects based on computer vision. An ant colony optimization edge detection algorithm based on automotive paint features (APF-ACO) is proposed. By combining global update and local update, the convergence speed of ant colony algorithm is improved and a new pheromone calculation and update method is proposed to effectively preserve the edge details of the detected image. A reflection area detection algorithm based on HSV color space is designed to detect the reflective area and eliminate interference. Establish defect classification identification rules, identify and mark five types of defects, and determine defect categories. Experiments show that the method can effectively detect the defect area and the recognition accuracy is 97.76%.
Similar content being viewed by others
References
Zhang J, Yin X, Luan J, Liu T (2019) An improved vehicle panoramic image generation algorithm. Multimed tools Appl 27663–27682. https://doi.org/10.1007/s11042-019-07890-w
Yin X, Zhang J, Wu X, Huang J, Xu Y, Zhu L (2019) An improved lane departure warning algorithm based on fusion of F-Kalman filter and F-TLC. Multimed Tools Appl 78:12203–12222. https://doi.org/10.1007/s11042-018-6762-2
Eichhorn A, Girimonte D, Klose A, Kruse R (2005) Soft computing for automated surface quality analysis of exterior car body panels. Appl Soft Comput J 5:301–313. https://doi.org/10.1016/j.asoc.2004.08.002
Chung YC, Chang M (2006) Visualization of subtle defects of car body outer panels. SICE-ICASE Int Jt Conf 2006:4639–4642. https://doi.org/10.1109/SICE.2006.315177
Puente León F, Kammel S (2006) Inspection of specular and painted surfaces with centralized fusion techniques. Meas J Int Meas Confed 39:536–546. https://doi.org/10.1016/j.measurement.2005.12.007
Borsu V, Yogeswaran A (2010) Payeur P (2010) automated surface deformations detection and marking on automotive body panels. IEEE Int Conf Autom Sci Eng CASE 2010:551–556. https://doi.org/10.1109/COASE.2010.5584643
Kamani P, Afshar A, Towhidkhah F, Roghani E (2011) Car body paint defect inspection using rotation invariant measure of the local variance and one-against-all support vector machine. Proc - 1st Int Conf informatics Comput Intell ICI 2011 244–249 . https://doi.org/10.1109/ICI.2011.47
Cheng P, Cui A, Yang Y, Luo Y, Sun W (2018) Recognition and classification of coating film defects on automobile body based on image processing. Proc - 2017 10th Int Congr Image Signal Process Biomed Eng Informatics, CISP-BMEI 2017 2018-Janua:1–5 . https://doi.org/10.1109/CISP-BMEI.2017.8302070
Edris MZB, Jawad MS, Zakaria Z (2016) Surface defect detection and neural network recognition of automotive body panels. Proc - 5th IEEE Int Conf control Syst Comput Eng ICCSCE 2015 117–122 . https://doi.org/10.1109/ICCSCE.2015.7482169
Jeyaraj PR, Samuel Nadar ER (2019) Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm. Int J Cloth Sci Technol 31:510–521. https://doi.org/10.1108/IJCST-11-2018-0135
Zhao L, Li F, Zhang Y, Xu X, Xiao H, Feng Y (2020) A deep-learning-based 3D defect quantitative inspection system in CC products surface. Sensors (Switzerland) 20: . https://doi.org/10.3390/s20040980
Wei X, Jiang S, Li Y, Li C, Jia L, Li Y (2020) Defect detection of pantograph slide based on deep learning and image processing technology. IEEE Trans Intell Transp Syst 21:947–958. https://doi.org/10.1109/TITS.2019.2900385
Palanikkumar D, Priya S (2018) Ant colony based graph theory (ACGT) and resource virtual network mapping (RVNM) algorithm for home healthcare system in cloud environment. Multimed Tools Appl 79:3743–3760. https://doi.org/10.1007/s11042-018-6908-2
Xu P (2019) Research on optimized model of travel route selection based on intelligent image information and ant Colony algorithm. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7539-y
Liantoni F, Perwira RI, Bataona DS (2018) Comparison of Adaptive Ant Colony Optimization for Image Edge Detection of Leaves Bone Structure. Emit Int J Eng Technol 6:328. https://doi.org/10.24003/emitter.v6i2.306
Giudice O, Allegra D, Stanco F, Grasso G, Battiato S (2018) A fast palette reordering technique based on GPU-optimized genetic algorithms. In: Proceedings - International Conference on Image Processing, ICIP. pp 1138–1142
Sun L, Kong X, Xu J, Xue Z, Zhai R, Zhang S (2019) A hybrid gene selection method based on ReliefF and ant Colony optimization algorithm for tumor classification. Sci Rep 9:1–14. https://doi.org/10.1038/s41598-019-45223-x
Yue L, Chen H (2019, 2019) unmanned vehicle path planning using a novel ant colony algorithm. EURASIP J Wirel Commun Netw 2019. https://doi.org/10.1186/s13638-019-1474-5
Jing L (2019) Defect detection and three dimensional reconstruction of castings. MATEC Web Conf 256:05001. https://doi.org/10.1051/matecconf/201925605001
Jiang J, Jin Z, Wang B, Ma L, Cui Y (2020) A sobel operator combined with patch statistics algorithm for fabric defect detection. KSII Trans Internet Inf Syst 14:687–701. https://doi.org/10.3837/tiis.2020.02.012
Li C, Gao G, Liu Z, Yu M, Huang D (2018) Fabric defect detection based on biological vision modeling. IEEE Access 6:27659–27670. https://doi.org/10.1109/ACCESS.2018.2841055
Σαλίχου Α (2012) Προηγμένες μέθοδοι βελτιστοποίσησης στη Διοίκηση Έργων. Η περίπτωση της βελτιστοποίησης με αποκίες μυρμηγκιών (Ant Colony Optimization) 1–96
Dorigo M, Maniezzo V, Colorni A (1999) Dorigo-Maniezzo-Colomi_the-ant-system-optimization-by-a-Colony-of-cooperating-agents. 26:1–26
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1:53–66. https://doi.org/10.1109/4235.585892
Jeleń Ł, Fevens T, Krzyzak A (2008) Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies. Int J Appl Math Comput Sci 18:75–83. https://doi.org/10.2478/v10006-008-0007-x
Zhang J, He K, Zheng X, Zhou J (2010) An ant colony optimization algorithm for image edge detection. Proc - Int Conf Artif Intell Comput Intell AICI 2:215–219. https://doi.org/10.1109/AICI.2010.167
Liu X, Fang S (2015) A convenient and robust edge detection method based on ant colony optimization. Opt Commun 353:147–157. https://doi.org/10.1016/j.optcom.2015.05.019
Kheirinejad S (2018) Max-min ant Colony optimization method for edge detection exploiting a new heuristic information function. 2018 8th Int Conf Comput Knowl Eng 12–15
Lin H, Shu N, Zhao CS (2003) A new edge evaluation method based on connection components. Mod Surv Mapp 26:8–11
Tao C, Xiankun S, Hua H, Xiaoming Y (2015) Image Edge Detection based on ACO-PSO Algorithm. Int J Adv Comput Sci Appl 6:47–54. https://doi.org/10.14569/ijacsa.2015.060708
Molina J, Solanes JE, Arnal L, Tornero J (2017) On the detection of defects on specular car body surfaces. Robot Comput Integr Manuf 48:263–278. https://doi.org/10.1016/j.rcim.2017.04.009
Tandiya A, Akthar S, Moussa M, Tarray C (2018) Automotive semi-specular surface defect detection system. In: proceedings - 2018 15th conference on computer and robot vision, CRV 2018. Pp 285–291
Acknowledgments
This work is supported by National Key Research and Development Program of China (2017YFB0102500), Natural Science Foundation of Jilin province (20170101133JC), Korea Foundation for Advanced Studies’ International Scholar Exchange Fellowship for the academic year of 2017-2018, the National Natural Science Foundation of China (61872158), Science and Technology Development Plan Project of Jilin Province (20190701019GH), the Fundamental Research Funds for the Central Universities, and Jilin University (5157050847, 2017XYB252).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Xu, J., Zhang, J., Zhang, K. et al. An APF-ACO algorithm for automatic defect detection on vehicle paint. Multimed Tools Appl 79, 25315–25333 (2020). https://doi.org/10.1007/s11042-020-09245-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-09245-2