DataVXJournal of Nondestructive EvaluationPaper
journal article·2025-06

Surface Defect Detection of Ultrathin Fiberboard Based on Improved YOLOv8x

Yang Long, Wenshu Lin

Journal of Nondestructive Evaluation· Volume 44· Issue 2
Publisher page
Bibliographic record

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · Advanced Measurement and Detection Methods

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (41)

Show source-provided references
1. Bin Roslan, M.I., Ibrahim, Z., Aziz, Z.A.: Real-time plastic surface defect detection using deep learning. In: 2022 IEEE 12th symposium on computer applications & industrial electronics (ISCAIE), pp. 111–116. Penang (2022). https://doi.org/10.1109/ISCAIE54458.2022.9794475.10.1109/ISCAIE54458.2022.9794475 ↗
2. Lu, L., Hou, J., Yuan, S., Yao, X., Li, Y., Zhu, J.: Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites. Robot. Comput. Integr. Manuf. 79, 102431 (2023). https://doi.org/10.1016/j.rcim.2022.10243110.1016/j.rcim.2022.102431 ↗
3. Gu, I.Y.H., Andersson, H., Vicen, R.: Wood defect classification based on image analysis and support vector machines. Wood Sci. Technol. 44(4), 693–704 (2010). https://doi.org/10.1007/s00226-009-0287-910.1007/s00226-009-0287-9 ↗
4. Cetiner, I., Var, A.A., Cetiner, H.: Classification of knot defect types using wavelets and k-nearest neighbors. Elektronika ir Elektrotechnika. 22(6), 67–72 (2016). https://doi.org/10.5755/j01.eie.22.6.1722710.5755/j01.eie.22.6.17227 ↗
5. Yang, Z., Zhang, M.M., Li, K., Chen, L.: Rapid detection of knot defects on wood surface by near infrared spectroscopy coupled with partial least squares discriminant analysis. BioResources. 11(1), 2557–2567 (2016)10.15376/biores.11.1.2557-2567 ↗
6. Li, C., Zhang, Y., Tu, W., Cao, J., Liang, H., Yu, H.: Soft measurement of wood defects based on LDA feature fusion and compressed sensor images. J. For. Res. 28(6), 1285–1292 (2017)10.1007/s11676-017-0395-6 ↗
7. Yang, Y., Zhou, X., Liu, Y., Hu, Z., Ding, F.: Wood defect detection based on depth extreme learning machine. Appl. Sci. 10(21), 7488 (2020)10.3390/app10217488 ↗
8. Aghamohammadi, A., Ranjbarzadeh, R., Naiemi, F., et al.: TPCNN: two-path convolutional neural network for surface defect detection in smart manufacturing. Expert. Syst. Appl. 183, 115406 (2021). https://doi.org/10.1016/j.eswa.2021.11540610.1016/j.eswa.2021.115406 ↗
9. Ren, Z., Fang, F., Yan, N., et al.: State of the art in defect detection based on machine vision. Int. J. of Precis. Eng. and Manuf.-Green Tech. 9, 661–691 (2022). https://doi.org/10.1007/s40684-021-00343-610.1007/s40684-021-00343-6 ↗
10. Fu, Y., Yao, X., Gao, X.: A Review on manufacturing defects and their detection of fiber reinforced resin matrix composites. Composites. Part. C: Open. Access. 8, 100276 (2021). https://doi.org/10.1016/j.jcomc.2021.10027610.1016/j.jcomc.2021.100276 ↗
11. Urbonas, A., Raudonis, V., Maskeliūnas, R., Damaševičius, R.: Automated identification of wood veneer surface defects using faster region-based convolutional neural network with data augmentation and transfer learning. Appl Sci. 9(22), 4898 (2019). https://doi.org/10.3390/app922489810.3390/app9224898 ↗
12. Wang, B., Yang, C., Ding, Y., Qin, G.: Detection of wood surface defects based on improved YOLOv3 algorithm. BioResources. 16(4), 6766–6780 (2021). https://doi.org/10.15376/biores.16.4.6766-678010.15376/biores.16.4.6766-6780 ↗
13. Zou, X., Wu, C., Liu, H., Yu, Z., Kuang, X.: An accurate object detection of wood defects using an improved Faster R-CNN model. Wood Mater. Sci. Eng. 1–7, (2024). https://doi.org/10.1080/17480272.2024.235260510.1080/17480272.2024.2352605 ↗
14. Zhu, Y.H., Xu, Z.Z., Lin, Y., Chen, D., Zheng, K.X., Yuan, Y.Z.: Surface defect detection of sawn timbers based on efficient multilevel feature integration. Meas. Sci. Technol. 35(4), 046101 (2024). https://doi.org/10.1088/1361-6501/ad15de10.1088/1361-6501/ad15de ↗
15. State Administration for Market Regulation (SAMR), & Standardization Administration of China (SAC). (2021). GB/T 20241-2021 Laminated veneer lumber (Adopted ISO/IEC international standards). [Note: For official publications, contact China Standards Press]. https://openstd.samr.gov.cn/bzgk/gb/newGbInfo?hcno=862614726C683FFA3503B9F02C5A25B1
16. General Administration of Quality Supervision, Inspection and Quarantine of the People’s Republic of China (AQSIQ), & Standardization Administration of China (SAC). (2017). GB/T 34722-2017 Surface decorated plywood and blockboard with paper impregnated thermosetting resins (Amendment No. 1) [Online]. Available: https://openstd.samr.gov.cn/bzgk/gb/newGbInfo?hcno=50A62315A7D7BDA202FC8F36112DA495
17. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.9110.1109/CVPR.2016.91 ↗
18. Jocher, G., Qiu, J., Chaurasia, A.: Ultralytics YOLO (Version 8.0.0) [Computer software] (2023). https://github.com/ultralytics/ultralytics
19. Telçeken, M., Akgun, D., Kacar, S.: An evaluation of image slicing and YOLO architectures for object detection in UAV images. Appl. Sci. 14(23), 11293 (2024). https://doi.org/10.3390/app14231129310.3390/app142311293 ↗
20. Tabernik, D., Šela, S., Skvarč, J., Skočaj, D.: Segmentation-based deep-learning approach for surface-defect detection. J. Intell. Manuf. 31(3), 759–776 (2020). https://doi.org/10.1007/s10845-019-01476-x10.1007/s10845-019-01476-x ↗
21. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp. 8759–8768. Salt Lake City, UT (2018). https://doi.org/10.1109/CVPR.2018.0091310.1109/CVPR.2018.00913 ↗
22. Ma, N., Zhang, X., Zheng, H.T., Sun, J.: ShuffleNet V2: practical guidelines for efficient cnn architecture design. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer vision – ECCV 2018. ECCV 2018, Lecture notes in computer science(), vol. 11218. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_810.1007/978-3-030-01264-9_8 ↗
23. Vandenhende, S., Georgoulis, S., Van Gansbeke, W., Proesmans, M., Dai, D., Van Gool, L.: Multi-task learning for dense prediction tasks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3614–3633 (2022). https://doi.org/10.1109/TPAMI.2021.305471910.1109/TPAMI.2021.3054719 ↗
24. Xu, H.: FCD-YOLO: improved YOLOv5 based on decoupled head and attention mechanism for defect detection on printed circuit board. In: 2nd international conference on networking systems of AI (INSAI), vol. 2022, pp. 7–11, Shanghai (2022). https://doi.org/10.1109/INSAI56792.2022.0001110.1109/INSAI56792.2022.00011 ↗
25. Wang, C.-Y., Mark Liao, H.-Y., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H.: CSPNet: a new backbone that can enhance learning capability of CNN. In: 2020 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp. 1571–1580, Seattle, WA (2020). https://doi.org/10.1109/CVPRW50498.2020.0020310.1109/CVPRW50498.2020.00203 ↗
26. He, L., Wang, M.: Slice samp: a promising down sampling alternative for retaining information in a neural network. Appl. Sci. 13(21), 11657 (2023). https://doi.org/10.3390/app13211165710.3390/app132111657 ↗
27. Zhang, H., An, L., Chu, V.W., Stow, D.A., Liu, X., Ding, Q.: Learning adjustable reduced downsampling network for small object detection in urban environments. Remote Sens. 13(18), 3608 (2021). https://doi.org/10.3390/rs1318360810.3390/rs13183608 ↗
28. Tan, M., Le, Q.V.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th international conference on machine learning (ICML 2019), pp. 6105–6114. PMLR (2019). https://doi.org/10.48550/arXiv.1905.1194610.48550/arXiv.1905.11946 ↗
29. Liu, X., H. Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y.: EfficientViT: memory efficient vision transformer with cascaded group attention. In: 2023 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp. 14420–14430. Vancouver, BC (2023). https://doi.org/10.1109/CVPR52729.2023.0138610.1109/CVPR52729.2023.01386 ↗
30. Xie, Y., Zhao, Y.: Lightweight improved YOLOv5 algorithm for PCB defect detection. J. Supercomput. 81, 261 (2025). https://doi.org/10.1007/s11227-024-06739-w10.1007/s11227-024-06739-w ↗
31. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 4510–4520 (2018). https://doi.org/10.1109/CVPR.2018.0047410.1109/CVPR.2018.00474 ↗
32. Li, X., Yang, J., et al.: Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection. In: Advances in neural information processing systems, vol. 33 (2020). https://doi.org/10.48550/arXiv.2006.0438810.48550/arXiv.2006.04388 ↗
33. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU loss: faster and better learning for bounding box regression. Proc. AAAI Conf. Artif. Intell. 34(7), 12993–13000 (2020). https://doi.org/10.1609/aaai.v34i07.699910.1609/aaai.v34i07.6999 ↗
34. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vision. 88(2), 303–338 (2010). https://doi.org/10.1007/s11263-009-0275-410.1007/s11263-009-0275-4 ↗
35. Tong, Z., Chen, Y., Xu, Z., Yu, R.: Wise-IoU: bounding box regression loss with dynamic focusing mechanism. arXiv preprint. (2022). https://doi.org/10.48550/arXiv.2301.1005110.48550/arXiv.2301.10051 ↗
36. Xiong, C., Abdelkader, E.M.: A novel YOLOv8-GAM-Wise-IoU model for automated detection of bridge surface cracks. Constr. Build. Mater. 414, 135025 (2024). https://doi.org/10.1016/j.conbuildmat.2024.13502510.1016/j.conbuildmat.2024.135025 ↗
37. Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), vol. 2020, pp. 10778–10787, Seattle, WA. https://doi.org/10.1109/CVPR42600.2020.0107910.1109/CVPR42600.2020.01079 ↗
38. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern. Anal. Mach. Intell. 39(6), 1137–1149 (2015). https://doi.org/10.1109/TPAMI.2016.257703110.1109/TPAMI.2016.2577031 ↗
39. Jocher, G.: YOLOv5 by Ultralytics (Version 7.0) [Computer software] (2020). https://doi.org/10.5281/zenodo.390855910.5281/zenodo.3908559 ↗
40. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696. (2022). https://doi.org/10.48550/arXiv.2207.0269610.48550/arXiv.2207.02696 ↗
41. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer vision – ECCV 2020. ECCV 2020. Lecture notes in computer science(), vol 12346. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_1310.1007/978-3-030-58452-8_13 ↗