Abstract
In order to reduce the influence of surface defects on the performance and appearance of hot-rolled steel strip, a surface defect detection method combining attention mechanism and multi-feature fusion network was proposed. In this method, the traditional SSD model was used as the basic framework, and the ResNet50 network after knowledge distillation was selected as the feature extraction network. The low-level features and high-level features were fused and complementary to improve the accuracy of detection. In addition, channel attention mechanism was introduced to filter and retain important information, which reduced the network computation and improves the network detection speed. The experimental results showed that the accuracy of RAF-SSD model for surface defect detection of hot rolled steel strip was significantly higher than that of traditional deep learning models, and the detection speed was 12.9% higher than that of SSD model, which can meet the real-time requirements of industrial detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xu, K., Wang, L., Wang, J.: Surface defect recognition of hot-rolled steel plates based on tetrolet transform. Instrum. Sci. Technol. 52(4), 13–19 (2016)
Wu, P., Lu, T., Wang, Y.: Nondestructive testing technique for strip surface defects and its applications. Nondestr. Test. 22(7), 312–315 (2000)
Zheng, J.: Research on strip surface defect detection method. Xian University of architecture and technology (2006)
Tao, X., Hou, W., Xu, D.: A survey of surface defect detection methods based on deep learning. Acta Automatica SinicamMonth (2020)
Jiang, J., Zhai, D.: Single-stage object detection algorithm based on atrous convolution and feature enhancement. Comput. Eng. (2020)
Zhu, D., Lin, Z.: Corn silk detection method based on MF-SSD convolutional neural network. J. South China Agricul. Univ. (2020)
Wu, S., Ding, E., Yu, X.: Foreign body identification of belt based on improved FPN. Saf. Coal Mines 50(12), 127–130 (2019)
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Ren, S.Q., He, K.M., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NIPS), Montreal, Quebec, Canada, pp. 91−99. MIT Press (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 779−788. IEEE (2016)
Ba, L.J., Caruana, R.: Do deep nets really need to be deep. In: Advances in Neural Information Processing Systems, pp. 2654–2662 (2013)
He, K.M., Zhang, X.Y., Ren, S.Q., et al.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Lei, P., Liu, C., Tang, J., et al.: Hierarchical feature fusion attention network for image super-resolution reconstruction. J. Image Graph. 9, 1773–1786 (2020)
Fu, C.Y., Liu, W., Ranga, A., et al.: DSSD: deconvolutional single shot detector. arXiv:1701.06659v1 (2017)
Hu, J., Li, S., Gang, S.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507 (2017)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, X., Gao, J. (2021). Surface Defect Detection Method of Hot Rolling Strip Based on Improved SSD Model. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021 International Workshops. DASFAA 2021. Lecture Notes in Computer Science(), vol 12680. Springer, Cham. https://doi.org/10.1007/978-3-030-73216-5_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-73216-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73215-8
Online ISBN: 978-3-030-73216-5
eBook Packages: Computer ScienceComputer Science (R0)