Abstract
Automated surface defect detection is crucial for ensuring product quality in industrial settings. This paper presents a multi-scale texture network that addresses this challenge by effectively analyzing textures at various scales. The proposed network incorporates a “Multi-Scale Texture Feature Processing” module to generate multi-scale texture tokens for comprehensive surface analysis. Additionally, a “Multi-Head Feature Encoding” mechanism captures local details and global patterns, leading to improved accuracy. Besides, we introduce a multi-scale perceptual loss function that guides training by optimizing images at different scales while preserving perceptual similarity. Experimental results demonstrate the effectiveness of our approach, offering high accuracy in automated surface defect detection.
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
Wang, H., Li, Z., Wang, H.: Few-shot steel surface defect detection. IEEE Trans. Instrum. Meas. (2021)
Hou, W., Jing, H.: Rc-yolov5s: for tile surface defect detection. Vis. Comput. (2023)
Tsai, D.-M., Fan, S.-K.S., Chou, Y.-H.: Auto-annotated deep segmentation for surface defect detection. IEEE Trans. Instrum. Meas. (2021)
Xiao, M., Yang, B., Wang, S., Zhang, Z., He, Y.: Fine coordinate attention for surface defect detection. Eng. Appl. Artif. Intell. (2023)
Zhou, H., Yang, R., Hu, R., Shu, C., Tang, X., Li, X.: Etdnet: efficient transformer-based detection network for surface defect detection. IEEE Trans. Instrum. Meas. (2023)
Wang, W., et al.: A real-time steel surface defect detection approach with high accuracy. IEEE Trans. Instrum. Meas. (2021)
Lian, J., He, J., Niu, Y., Wang, T.: Fast and accurate detection of surface defect based on improved yolov4. Robot. Intell. Autom. (2021)
Xie, Y., Hu, W., Xie, S., He, L.: Surface defect detection algorithm based on feature-enhanced yolo. Cogn. Comput. (2022)
Yeung, C.-C., Lam, K.-M.: Efficient fused-attention model for steel surface defect detection. IEEE Trans. Instrum. Meas. (2022)
Liu, T., He, Z., Lin, Z., Cao, G.-Z., Su, W., Xie, S.: An adaptive image segmentation network for surface defect detection. IEEE Trans. Neural Netw. Learn. Syst. (2022)
Jain, S., Seth, G., Paruthi, A., Soni, U., Kumar, G.: Synthetic data augmentation for surface defect detection and classification using deep learning. J. Intell. Manuf. (2020)
Li, Z., Wei, X., Hassaballah, M., Li, Y., Jiang, X.: A deep learning model for steel surface defect detection. Complex Intell. Syst. (2023)
Feng, X., Gao, X., Luo, L.: X-SDD: a new benchmark for hot rolled steel strip surface defects detection. Symmetry (2021)
Wang, J., Zhang, Q., Liu, G., DRCDCT-net: a steel surface defect diagnosis method based on a dual-route cross-domain convolution-transformer network. Meas. Sci. Technol. (2022)
Li, S., Wu, C., Xiong, N.: Hybrid architecture based on CNN and transformer for strip steel surface defect classification. Electronics (2022)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Xie, S., Girshick, R., Dollar, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986 (2022)
Yu, W., Zhou, P., Yan, S., Wang, X.: Inceptionnext: when inception meets convnext. arXiv preprint arXiv:2303.16900 (2023)
Acknowledgment
The authors declare no conflict of interest exists in the submission of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, L., Huang, Y., Kong, F. (2024). Multi-scale Texture Network for Industrial Surface Defect Detection. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2015. Springer, Singapore. https://doi.org/10.1007/978-981-97-0827-7_16
Download citation
DOI: https://doi.org/10.1007/978-981-97-0827-7_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0826-0
Online ISBN: 978-981-97-0827-7
eBook Packages: Computer ScienceComputer Science (R0)