Abstract
Quantity control is as important as quality control for those products manufactured in the mass production phase. However, there is scarce work implementing deep learning methods in the manufacturing line, especially in wiper arm recognition. This paper proposed a deep learning-based wiper arm recognition for the windshield wiper manufacturer to reduce human error and workforce requirement in quantity control. The proposed method applied the state-of-the-art YOLOv4 object detection algorithm. Our proposed method able to achieve 100% in terms of precision, recall, F1-score, and mean average precision. Moreover, the proposed method can make correct predictions under several conditions: object occlusion, different scales of objects, and different light environments. In term of speed, the proposed method can be predicted up to 30.55 fps when using a moderate GPU.
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
References
Okeafor JP: How many cars are there in the world. https://naijauto.com/market-news/how-many-cars-are-there-in-the-world-7100.
Organisation Internationale des Constructeurs d’Automobiles: 2020 Production Statistics. https://www.oica.net/category/production-statistics/2020-statistics/.
Parts, D.C.S.: Are Wiper Blades Universal. https://www.diycarserviceparts.co.uk/blog/2019/08/26/wiper-blade-types-are-wiper-blades-universal/.
O’Riordan, A.D., Toal, D., Newe, T., Dooly, G.: Object recognition within smart manufacturing. Procedia Manuf. Procedia Manuf. 38, 408–414 (2019). https://doi.org/10.1016/j.promfg.2020.01.052
Apostolopoulos, I.D., Tzani, M.: Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multi-level VGG19. ArXiv preprint, pp. 1–17 (2020)
Caggiano, A., Zhang, J., Alfieri, V., Caiazzo, F., Gao, R., Teti, R.: Machine learning-based image processing for on-line defect recognition in additive manufacturing. Procedia CIRP 68, 451–454 (2019). https://doi.org/10.1016/j.cirp.2019.03.021
Gu, J., et al.: Recent advances in convolutional neural networks. Patt. Recogn. 77, 354–377 (2015)
Wang, P., Liu, H., Wang, L., Gao, R.X.: Deep learning-based human motion recognition for predictive context-aware human-robot collaboration. Procedia CIRP. 67, 17–20 (2018). https://doi.org/10.1016/j.cirp.2018.04.066
Wang, J., Ma, Y., Zhang, L., Gao, R.X., Wu, D.: Deep learning for smart manufacturing: Methods and applications. J. Manuf. Syst. 48, 144–156 (2018). https://doi.org/10.1016/j.jmsy.2018.01.003
Fu, G., et al.: A deep-learning-based approach for fast and robust steel surface defects classification. Opt. Lasers Eng. 121, 397–405 (2019). https://doi.org/10.1016/j.optlaseng.2019.05.005
Wang, J., Fu, P., Gao, R.X.: Machine vision intelligence for product defect inspection based on deep learning and Hough transform. J. Manuf. Syst. 51, 52–60 (2019). https://doi.org/10.1016/j.jmsy.2019.03.002
Wei, Y., Tran, S., Xu, S., Kang, B., Springer, M.: Deep learning for retail product recognition: challenges and techniques. Comput. Intell. Neurosci. 2020 (2020). https://doi.org/10.1155/2020/8875910.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 6517–6525, January 2017. https://doi.org/10.1109/CVPR.2017.690.
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38, 142–158 (2016). https://doi.org/10.1109/TPAMI.2015.2437384
Msonda, P., Uymaz, S.A., Karaaǧaç, S.S.: Spatial pyramid pooling in deep convolutional networks for automatic tuberculosis diagnosis. Traitement du Signal. 37, 1075–1084 (2020). https://doi.org/10.18280/TS.370620.
Girshick, R.: Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015). https://doi.org/10.1109/ICCV.2015.169
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91
Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. ArXiv preprint (2018)
Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: Optimal Speed and Accuracy of Object Detection. ArXiv preprint (2020)
Ghiasi, G., Lin, T.-Y., Le, Q.V.: DropBlock: A regularization method for convolutional networks. ArXiv preprint (2018)
Acknowledgments
The authors are grateful to DENSO WIPER SYSTEMS (M) SDN BHD for providing the wiper arms used in this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ling, H.J., Goh, K.M., Lai, W.K. (2021). Wiper Arm Recognition Using YOLOv4. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13110. Springer, Cham. https://doi.org/10.1007/978-3-030-92238-2_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-92238-2_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92237-5
Online ISBN: 978-3-030-92238-2
eBook Packages: Computer ScienceComputer Science (R0)