Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n
Source abstract
Abstract
The traditional detection methods of steel surface defects have some problems, such as a lack of feature extraction ability, sluggish detection speed, and subpar detection performance. In this paper, a YOLOv8-based DDI-YOLO model is suggested for effective steel surface defect detection. First, on the Backbone network, the extended residual module (DWR) is fused with the C2f module to obtain C2f_DWR, and the two-step approach is used to carry out the effective extraction of multiscale contextual information, and then fusing feature maps formed from the multiscale receptive fields to enhance the capacity for feature extraction. Also based on the above, an extended heavy parameter module (DRB) is added to the structure of C2f_DWR to make up for the lack of C2f’s ability to capture small-scale pattern defects between training to enhance the training fluency of the model. Finally, the Inner-IoU loss function is employed to enhance the regression accuracy and training speed of the model. The experimental results show that the detection of DDI-YOLO on the NEU-DET dataset improves the mAP by 2.4%, the accuracy by 3.3%, and the FPS by 59 frames/s compared with the original YOLOv8n.Therefore, this paper’s proposed model has a superior mAP, accuracy, and FPS in identifying surface defects in steel.
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
School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Funding
National Natural Science Foundation of China · National Key Research and Development Program of China · Natural Science Foundation of Liaoning Province · Shenyang Outstanding Young and Middle-aged Science and Technology Talents Project · Basic Scientific Research Project of Liaoning Provincial Department of Education
References (34)
Show source-provided references
1. Yu, Q., Wu, Q., and Liu, H. (2022, January 2–4). Research on X-Ray Contraband Detection and Overlapping Target Detection Based on Convolutional Network. Proceedings of the 2022 4th International Conference on Frontiers Technology of Information and Computer (ICFTIC), Qingdao, China.10.1109/ICFTIC57696.2022.10075330 ↗
2. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
3. SSD: Single Shot MultiBox Detector10.1007/978-3-319-46448-0_2 ↗
4. U-Net: Convolutional Networks for Biomedical Image Segmentation
5. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
6. Girshick, R. (2015, January 7–13). Fast R-Cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
7. Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017, January 22–29). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.324 ↗
8. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks10.1109/TPAMI.2016.2577031 ↗
9. He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017, January 22–29). Mask R-Cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.322 ↗
10. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention Is All You Need. arXiv.
11. End-to-End Object Detection with Transformers
12. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image Is Worth 16 × 16 Words: Transformers for Image Recognition at Scale. arXiv.
13. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11–17). Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows. Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada.10.1109/ICCV48922.2021.00986 ↗
14. Radford, A., Metz, L., and Chintala, S. (2016). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv.
15. (2024, June 08). Applied Sciences|Free Full-Text|A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles. Available online: https://www.mdpi.com/2076-3417/14/1/386.10.3390/app14010386 ↗
16. Steel Surface Defect Detection Using Improved Deep Learning Algorithm: ECA-SimSPPF-SIoU-Yolov510.1109/ACCESS.2024.3371584 ↗
17. Small sample steel plate defect detection algorithm of lightweight YOLOv810.54254/2755-2721/42/20230694 ↗
18. Guo, Z., Wang, C., Yang, G., Huang, Z., and Li, G. (2022). Msft-Yolo: Improved Yolov5 Based on Transformer for Detecting Defects of Steel Surface. Sensors, 22.10.3390/s22093467 ↗
19. Steel surface defect detection algorithm based on MCB-FAH-YOLOv8
20. Steel defect detection based on multi-scale lightweight attention
21. Surface Defect Detection and Classification of Steel Using an Efficient Swin Transformer10.1016/j.aei.2023.102061 ↗
22. An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features10.1109/TIM.2019.2915404 ↗
23. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv.
24. Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv.
25. Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv.
26. Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.
27. Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J., Yifu, Z., Wong, C., and Montes, D. (2022). Ultralytics/Yolov5: V7. 0-Yolov5 Sota Realtime Instance Segmentation. Zenodo.
28. Wei, H., Liu, X., Xu, S., Dai, Z., Dai, Y., and Xu, X. (2023). DWRSeg: Rethinking Efficient Acquisition of Multi-Scale Contextual Information for Real-Time Semantic Segmentation. arXiv.
29. Ding, X., Zhang, Y., Ge, Y., Zhao, S., Song, L., Yue, X., and Shan, Y. (2024). UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition. arXiv.10.1109/CVPR52733.2024.00527 ↗
30. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7–12). Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
31. Zhang, H., Xu, C., and Zhang, S. (2023). Inner-IoU: More Effective Intersection over Union Loss with Auxiliary Bounding Box. arXiv.
32. A Noise Robust Method Based on Completed Local Binary Patterns for Hot-Rolled Steel Strip Surface Defects10.1016/j.apsusc.2013.09.002 ↗
33. Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 17–24). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00721 ↗
34. Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024). DETRs Beat YOLOs on Real-Time Object Detection. arXiv.10.1109/CVPR52733.2024.01605 ↗