Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
Source abstract
Abstract
Detecting steel defects is a vital process in industrial production, but traditional methods suffer from poor feature extraction and low detection accuracy. To address these issues, this research introduces an improved model, EB-YOLOv8, based on YOLOv8. First, the multi-scale attention mechanism EMA is integrated into the backbone and neck sections to reduce noise during gradient descent and enhance model stability by encoding global information and weighting model parameters. Second, the weighted fusion splicing module, Concat_BiFPN, is used in the neck network to facilitate multi-scale feature detection and fusion. This improves detection precision. The results show that the EB-YOLOv8 model increases detection accuracy on the NEU-DET dataset by 3.1%, reaching 80.2%, compared to YOLOv8. Additionally, the average precision on the Severstal steel defect dataset improves from 65.4% to 66.1%. Overall, the proposed model demonstrates superior recognition performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · Surface Roughness and Optical Measurements
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Jilin Provincial Key Laboratory for Numerical Simulation, Jilin Normal University, Siping 136000, China
School of Mathematics and Computer Science, Jilin Normal University, Siping 136000, China
Teacher Development Center, Jilin Normal University, Siping 136000, China
Funding
National Natural Science Foundation of China · Innovative and Entrepreneurial Talents Foundation of Jilin Province · Natural Science Foundation of Jilin Province
References (30)
Show source-provided references
1. Automated Visual Defect Detection for Flat Steel Surface: A Survey10.1109/TIM.2019.2963555 ↗
2. Development of a YOLO-V3-based model for detecting defects on steel strip surface10.1016/j.measurement.2021.109454 ↗
3. Deep learning in alloy material microstructures: Application and prospects10.1016/j.mtcomm.2023.107531 ↗
4. Surface defect detection of steel strips based on improved YOLOv410.1016/j.compeleceng.2022.108208 ↗
5. Microstructure and mechanical properties of 6061 aluminum alloy laser-MIG hybrid welding joint10.1007/s11771-022-4954-4 ↗
6. Intuitionistic fuzzy divergence for evaluating the mechanical stress state of steel plates subject to bi-axial loads10.3233/ICA-230730 ↗
7. An integrated production batch planning approach for steelmaking-continuous casting with cast batching plan as the core10.1016/j.cie.2022.108636 ↗
8. Zeng, K., Xia, Z., Qian, J., Du, X., Xiao, P., and Zhu, L. (2025). Steel Surface Defect Detection Technology Based on YOLOv8-MGVS. Metals, 15.10.3390/met15020109 ↗
9. Image-Based Surface Defect Detection Using Deep Learning: A Review10.1115/1.4049535 ↗
10. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.10.1109/CVPR.2016.91 ↗
11. SSD: Single Shot MultiBox Detector10.1007/978-3-319-46448-0_2 ↗
12. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv.10.1109/CVPR.2014.81 ↗
13. Ren, S., He, K., Girshick, R., and Sun, J. (2016). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv.10.1109/TPAMI.2016.2577031 ↗
14. Anantharaman, R., Velazquez, M., and Lee, Y. (2018, January 3–6). Utilizing Mask R-CNN for Detection and Segmentation of Oral Diseases. Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain.10.1109/BIBM.2018.8621112 ↗
15. Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2018). Focal Loss for Dense Object Detection. arXiv.10.1109/ICCV.2017.324 ↗
16. Han, O.C., and Kutbay, U. (2025). Detection of Defects on Metal Surfaces Based on Deep Learning. Appl. Sci., 15.10.3390/app15031406 ↗
17. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
18. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.10.1109/CVPR.2015.7298594 ↗
19. Experimental Study on YOLO-Based Leather Surface Defect Detection10.1109/ACCESS.2024.3369705 ↗
20. Ishtiaque Mahbub, A.M., and Malikopoulos, A.A. (2022, January 8–10). Platoon Formation in a Mixed Traffic Environment: A Model-Agnostic Optimal Control Approach. Proceedings of the 2022 American Control Conference (ACC), Atlanta, GA, USA.10.23919/ACC53348.2022.9867168 ↗
21. Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.10.1109/CVPR52729.2023.00721 ↗
22. Ouyang, D., He, S., Zhang, G., Luo, M., Guo, H., Zhan, J., and Huang, Z. (2023, January 4–10). Efficient Multi-Scale Attention Module with Cross-Spatial Learning. Proceedings of the ICASSP 2023—2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.10.1109/ICASSP49357.2023.10096516 ↗
23. Tan, M., Pang, R., and Le, Q.V. (2020). EfficientDet: Scalable and Efficient Object Detection. arXiv.10.1109/CVPR42600.2020.01079 ↗
24. DES-YOLO: A novel model for real-time detection of casting surface defects10.7717/peerj-cs.2224 ↗
25. Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018). Path Aggregation Network for Instance Segmentation. arXiv.10.1109/CVPR.2018.00913 ↗
26. An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features10.1109/TIM.2019.2915404 ↗
27. (2024, October 25). Severstal Steel Defect Dataset 2020. Available online: https://gitcode.com/open-source-toolkit/f4425/blob/main/severstal-steel-defect-detection.zip.
28. Davis, J., and Goadrich, M. (2006, January 25–29). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning—ICML ’06, Pittsburgh, PA, USA.10.1145/1143844.1143874 ↗
29. Ma, S., Zhao, X., Wan, L., Zhang, Y., and Gao, H. (2025). A lightweight algorithm for steel surface defect detection using improved YOLOv8. Sci. Rep., 15.10.1038/s41598-025-93469-5 ↗
30. Improving the lightweight FCM-YOLOv8n for steel surface defect detection