Abstract
When the steel leaf spring gras** robot grasps the steel leaf spring, it needs to identify the steel leaf spring first and then obtain the spatial position information of the target steel leaf spring, so that the robot can obtain the best motion trajectory and improve the efficiency of steel leaf spring gras**. In this paper, the identification method of steel leaf springs is studied. The images of the steel leaf springs are first acquired using the camera, and then the steel leaf springs are recognized using a modified YOLOv5s network. The experimental results demonstrate the effectiveness of the improved YOLOv5s network, with a 60.4% reduction in model size, has an average detection speed of 78.7 f/s and an average detection accuracy of 90.7%.The steel leaf spring identification method studied in this paper meets the requirements of the steel leaf spring grip** robot in terms of speed and accuracy, and provides a reference for the identification of other industrial parts.
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
Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 413–420 (2009)
Cheng, X., Lu, J., Feng, J., et al.: Scene recognition with objectness. Pattern Recogn. 74, 474–487 (2018)
Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate objectdetection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 580–587 (2014)
Redmon, J., Divvala, S., Girshick, R., et al.: 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)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
Yang, Y., Li, D.: Lightweight helmet wearing detection algorithm of improved YOLOv5. Comput. Eng. Appl. 58(09), 201–207 (2022)
Yang, Q., Li, W., Yang, X., et al.: Improved YOLOv5 method for detecting growth status of apple flowers. Comput. Eng. Appl. 58(04), 237–246 (2022)
Yan, B., Fan, P., Lei, X., et al.: A real-time apple targets detection method for picking robot based on improved YOLOv5. Remote Sens. 13(9), 1619 (2021)
Liu, J., Zhong, G., Huang, S., et al.: Vehicle attribute detection based on improved YOLOv5. Appl. Electron. Tech. 48(7), 19–24 (2022)
Li, R., Qian, H., Guo, J., et al.: Lightweight target detection algorithmbased on M-YOLOv4 model. Foreign Electron. Meas. Technol. 41(04), 15–21 (2022)
Li, Y., Zhang, C., Zhao, Y., et al.: Research on lightweight obstacle detection model based on model compression. Laser J. 43(09), 38–43 (2022)
Avazov, K., Mukhiddinov, M., Makhmudov, F., et al.: Fire detection method in smart city environments using a deep-learning-based approach. Electronics 11(1), 73 (2021)
Liu, S., Zhang, N., Yu, G.: Lightweight security wear detection method based on YOLOv5. Wirel. Commun. Mob. Comput. 2022 (2022)
Krizhevsky, A., Sutskever, I., Hinton, G., E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Lin, T.Y., Maire, M., Belongie, S., et al.: Microsoft coco: common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, 6–12 September 2014, Proceedings, Part V 13, pp. 740–755 (2014)
Liu, J., Zhong, G., Huang, S., et al.: Vehicle attribute detection based on improved YOLOv5. Appl. Electron. Techn. 48(7), 19–24, 29 (2022)
Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big data 3(1), 1–40 (2016)
Jiang, P., Ergu, D., Liu, F., et al.: A review of yolo algorithm developments. Procedia Comput. Sci. 199, 1066–1073 (2022)
Woo, S., Park, J., Lee, J., Y., et al.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19 (2018)
Lin, S., Liu, M., Tao, Z.: Detection of underwater treasures using attention mechanism and improved YOLOv5. Trans. Chin. Soc. Agr. Eng. (Trans. CSAE) 37(18), 307–314 (2021)
Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 413–420 (2009)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gao, C., Lu, S., Gu, X., Hu, X. (2023). Improved YOLOv5s Based Steel Leaf Spring Identification. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1870. Springer, Singapore. https://doi.org/10.1007/978-981-99-5847-4_1
Download citation
DOI: https://doi.org/10.1007/978-981-99-5847-4_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5846-7
Online ISBN: 978-981-99-5847-4
eBook Packages: Computer ScienceComputer Science (R0)