journal article·2025-09

Application of the Improved YOLOv8 Algorithm for Small Object Detection in X-ray Weld Inspection Images

Wenwen Lu, Haoyuan Zheng, Shouzhen Xiao, Weihua Xue, Shaobin Yang

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OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · Welding Techniques and Residual Stresses

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References (58)

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1. Say, D., Zidi, S., Qaisar, S.M., et al.: Automated categorization of multiclass welding defects using the x-ray image augmentation and convolutional neural network[J]. Sensors. 23(14), 6422 (2023). https://doi.org/10.3390/s2314642210.3390/s23146422 ↗
2. Kah, P., Rajan, R., Martikainen, J., et al.: Investigation of weld defects in friction-stir welding and fusion welding of aluminium alloys. Int. J. Mech. Mater. Eng. 10, 26 (2015). https://doi.org/10.1186/s40712-015-0053-810.1186/s40712-015-0053-8 ↗
3. Wang, G., Liao, T.W., Ndt: E Int. 35(8), 519–528 (2002). https://doi.org/10.1016/S0963-8695(02)00025-710.1016/S0963-8695(02)00025-7 ↗
4. Gupta, M., Khan, M.A., Butola, R., et al.: Advances in applications of Non-Destructive testing (NDT): A review[J]. Adv. Mater. Process. Technol. 8(2), 2286–2307 (2022). https://doi.org/10.1080/2374068X.2021.190933210.1080/2374068X.2021.1909332 ↗
5. Lim, M.K., Cao, H.: Combining multiple NDT methods to improve testing effectiveness[J]. Constr. Build. Mater. 38, 1310–1315 (2013). https://doi.org/10.1016/j.conbuildmat.2011.01.01110.1016/j.conbuildmat.2011.01.011 ↗
6. Hou, W., Zhang, D., Wei, Y., et al.: Review on computer aided weld defect detection from radiography images[J]. Appl. Sci. 10(5), 1878 (2020). https://doi.org/10.3390/app1005187810.3390/app10051878 ↗
7. Yang, D., Cui, Y., Yu, Z., et al.: Deep learning based steel pipe weld defect detection[J]. Appl. Artif. Intell. 35(15), 1237–1249 (2021). https://doi.org/10.1080/08839514.2021.197539110.1080/08839514.2021.1975391 ↗
8. Wang, J., Fu, P., Gao, R.X.: Machine vision intelligence for product defect inspection based on deep learning and Hough transform[J]. J. Manuf. Syst. 51, 52–60 (2019). https://doi.org/10.1016/j.jmsy.2019.03.00210.1016/j.jmsy.2019.03.002 ↗
9. Ren, Z., Fang, F., Yan, N., et al.: State of the Art in defect detection based on machine vision[J]. Int. J. Precision Eng. Manufacturing-Green Technol. 9(2), 661–691 (2022). https://doi.org/10.1007/s40684-021-00343-610.1007/s40684-021-00343-6 ↗
10. Shi, F., Lin, T., Chen, S.: Efficient weld seam detection for robotic welding based on local image processing[J]. Industrial Robot: Int. J. 36(3), 277–283 (2009). https://doi.org/10.1108/0143991091095055910.1108/01439910910950559 ↗
11. Mahmoudi, A., Regragui, F.: Sci. Inform. Eng. IEEE. 7, 111–115 (2009). https://doi.org/10.1109/CSIE.2009.501 Welding defect detection by segmentation of radiographic images[C]//2009 WRI World Congress on Computer10.1109/CSIE.2009.501 ↗
12. Mery, D., Berti, M.A.: Automatic detection of welding defects using texture features[J]. Insight-Non-Destructive Test. Condition Monit. 45(10), 676–681 (2003). https://doi.org/10.1784/insi.45.10.676.5295210.1784/insi.45.10.676.52952 ↗
13. Sun, H., Ramuhalli, P., Jacob, R.E.: Machine learning for ultrasonic nondestructive examination of welding defects: A systematic review[J]. Ultrasonics. 127, 106854 (2023). https://doi.org/10.1016/j.ultras.2022.10685410.1016/j.ultras.2022.106854 ↗
14. Abdullah, D.M., Abdulazeez, A.M.: Machine learning applications based on SVM classification a review[J]. Qubahan Acad. J. 1(2), 81–90 (2021). https://doi.org/10.48161/qaj.v1n2a5010.48161/qaj.v1n2a50 ↗
15. Alzubaidi, L., Zhang, J., Humaidi, A.J., et al.: Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions[J]. J. Big Data. 8, 1–74 (2021). https://doi.org/10.1186/s40537-021-00444-810.1186/s40537-021-00444-8 ↗
16. Jiang, P., Ergu, D., Liu, F., et al.: A review of Yolo algorithm developments[J]. Procedia Comput. Sci. 199, 1066–1073 (2022). https://doi.org/10.1016/j.procs.2022.01.13510.1016/j.procs.2022.01.135 ↗
17. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation[C]//Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, proceedings, part III 18. Springer international publishing,: 234–241. (2015). https://doi.org/10.48550/arXiv.1505.0459710.48550/arXiv.1505.04597 ↗
18. Terven, J., Córdova-Esparza, D.M., Romero-González, J.A.: A comprehensive review of Yolo architectures in computer vision: From Yolov1 to Yolov8 and Yolo-nas[J]. Mach. Learn. Knowl. Extr. 5(4), 1680–1716 (2023). https://doi.org/10.3390/make504008310.3390/make5040083 ↗
19. Lin, T.Y., Maire, M., Belongie, S., et al.: Microsoft coco: Common objects in context[C]//Computer Vision–ECCV.: 13th European Conference, Zurich, Switzerland, September 6–12, 2014, Proceedings, Part V 13. Springer International Publishing, 2014: 740–755. (2014). https://doi.org/10.1007/978-3-319-10602-1_4810.1007/978-3-319-10602-1_48 ↗
20. Nie, H., Pang, H., Ma, M., et al.: A lightweight remote sensing small target image detection algorithm based on improved YOLOv8[J]. Sensors. 24(9), 2952 (2024). https://doi.org/10.3390/s2409295210.3390/s24092952 ↗
21. Hou, W., Wei, Y., Guo, J., et al.: Automatic detection of welding defects using deep neural network[C]//Journal of physics: Conference series. IOP Publishing, 933: 012006. (2018). https://doi.org/10.1088/1742-6596/933/1/01200610.1088/1742-6596/933/1/012006 ↗
22. Wang, Y., Shi, F., Tong, X.: A welding defect identification approach in X-ray images based on deep convolutional neural networks[C]//Intelligent Computing Methodologies: 15th International Conference, ICIC 2019, Nanchang, China, August 3–6, 2019, Proceedings, Part III 15. Springer International Publishing,: 53–64. (2019). https://doi.org/10.1007/978-3-030-26766-7_610.1007/978-3-030-26766-7_6 ↗
23. Tong, K., Wu, Y., Zhou, F.: Recent advances in small object detection based on deep learning: A review[J]. Image Vis. Comput. 97, 103910 (2020). https://doi.org/10.1016/j.imavis.2020.10391010.1016/j.imavis.2020.103910 ↗
24. Hussain, M.: YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection[J]. Machines. 11(7), 677 (2023). https://doi.org/10.3390/machines1107067710.3390/machines11070677 ↗
25. Alif, M.A.R., Hussain, M.: YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain[J]. ArXiv Preprint ArXiv:2406.10139, (2024). https://doi.org/10.48550/arXiv.2406.1013910.48550/arXiv.2406.10139 ↗
26. Liu, Z., Ye, K.: YOLO-IMF: an improved YOLOv8 algorithm for surface defect detection in industrial manufacturing field[C]//International Conference on Metaverse. Cham: Springer Nature Switzerland,: 15–28. (2023). https://doi.org/10.1007/978-3-031-44754-9_210.1007/978-3-031-44754-9_2 ↗
27. Fan, Y., Zhang, L., Li, P.: A lightweight model of underwater object detection based on YOLOv8n for an edge computing Platform[J]. J. Mar. Sci. Eng. 12(5), 697 (2024). https://doi.org/10.3390/jmse1205069710.3390/jmse12050697 ↗
28. Sohan, M., Sai Ram, T., Reddy, R., A review on yolov8 and its advancements[C]//International Conference on Data Intelligence and, Informatics, C., et al.: Springer, Singapore,: 529–545. (2024). https://doi.org/10.1007/978-981-99-7962-2_3910.1007/978-981-99-7962-2_39 ↗
29. Gai, R., Liu, Y., Xu, G.: TL-YOLOv8: A blueberry fruit detection algorithm based on improved YOLOv8 and transfer learning[J]. IEEE Access. (2024). https://doi.org/10.1109/ACCESS.2024.341633210.1109/ACCESS.2024.3416332 ↗
30. Wang, Y., Zhang, K., Wang, L., et al.: An improved YOLOv8 algorithm for rail surface defect detection[J]. IEEE Access. (2024). https://doi.org/10.1109/ACCESS.2024.338000910.1109/ACCESS.2024.3380009 ↗
31. Lin, B.: Safety helmet detection based on improved YOLOv8[J]. IEEE Access. (2024). https://doi.org/10.1109/ACCESS.2024.336816110.1109/ACCESS.2024.3368161 ↗
32. Wu, T., Dong, Y., YOLO-SE: Improved YOLOv8 for remote sensing object detection and recognition[J]. Appl. Sci. 13(24), 12977 (2023). https://doi.org/10.3390/app13241297710.3390/app132412977 ↗
33. Zhai, X., Huang, Z., Li, T., et al.: YOLO-Drone: An optimized YOLOv8 network for tiny UAV object detection[J]. Electronics. 12(17), 3664 (2023). https://doi.org/10.3390/electronics1217366410.3390/electronics12173664 ↗
34. Feng, F., Hu, Y., Li, W., et al.: Improved YOLOv8 algorithms for small object detection in aerial imagery[J]. J. King Saud University-Computer Inform. Sci. 36(6), 102113 (2024). https://doi.org/10.1016/j.jksuci.2024.10211310.1016/j.jksuci.2024.102113 ↗
35. He, M., Qin, L., Deng, X., et al.: MFI-YOLO: multi-fault insulator detection based on an improved YOLOv8[J]. IEEE Trans. Power Delivery. (2023). https://doi.org/10.1109/TPWRD.2023.332817810.1109/TPWRD.2023.3328178 ↗
36. Su, Z., Yu, J., Tan, H., et al.: Msa-yolo: A remote sensing object detection model based on multi-scale strip attention[J]. Sensors. 23(15), 6811 (2023). https://doi.org/10.3390/s2315681110.3390/s23156811 ↗
37. Wang, W., Chen, J., Han, G., et al.: Application of object detection algorithms in Non-Destructive testing of pressure equipment: A Review[J]. Sensors. 24(18), 5944 (2024). https://doi.org/10.3390/s2418594410.3390/s24185944 ↗
38. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation[C]//Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, proceedings, part III 18. Springer International Publishing,: 234–241. (2015). https://doi.org/10.1007/978-3-319-24574-4_2810.1007/978-3-319-24574-4_28 ↗
39. Liu, Y., Sun, P., Wergeles, N., et al.: A survey and performance evaluation of deep learning methods for small object detection[J]. Expert Syst. Appl. 172, 114602 (2021). https://doi.org/10.1016/j.eswa.2021.11460210.1016/j.eswa.2021.114602 ↗
40. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.: 7132–7141. (2018). https://doi.org/10.48550/arXiv.1709.0150710.48550/arXiv.1709.01507 ↗
41. Fang, Y., Pang, H.: An improved pedestrian detection model based on YOLOv8 for dense Scenes[J]. Symmetry. 16(6), 716 (2024). https://doi.org/10.3390/sym1606071610.3390/sym16060716 ↗
42. Qi, Y., He, Y., Qi, X., et al.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.: 6070–6079. (2023). https://doi.org/10.48550/arXiv.2307.0838810.48550/arXiv.2307.08388 ↗
43. Wang, K., Liew, J.H., Zou, Y., et al.: Panet: Few-shot image semantic segmentation with prototype alignment[C]//proceedings of the IEEE/CVF international conference on computer vision.: 9197–9206. (2019). https://doi.org/10.1109/ICCV.2019.0092910.1109/ICCV.2019.00929 ↗
44. Tan, M., Pang, R., Le, Q.V., Efficientdet: Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.: 10781–10790. (2020). https://doi.org/10.1109/CVPR42600.2020.0107910.1109/CVPR42600.2020.01079 ↗
45. Tong, Z., Chen, Y., Xu, Z., et al.: Wise-IoU: Bounding box regression loss with dynamic focusing mechanism. ArXiv 2023[J]. ArXiv preprint arXiv:2301.10051. https://doi.org/10.48550/arXiv.2301.1005110.48550/arXiv.2301.10051 ↗
46. Wang, H., Yang, H., Chen, H., et al.: A remote sensing image target detection algorithm based on improved YOLOv8[J]. Appl. Sci. 14(4), 1557 (2024). https://doi.org/10.3390/app1404155710.3390/app14041557 ↗
47. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020. https://doi.org/10.48550/arXiv.2004.1093410.48550/arXiv.2004.10934 ↗
48. Jiang, Z., Zhao, L., Li, S., et al.: Real-time object detection method based on improved YOLOv4-tiny[J]. arXiv preprint arXiv:2011.04244, 2020. https://doi.org/10.48550/arXiv.2011.0424410.48550/arXiv.2011.04244 ↗
49. Guo, C., Lv, X., Zhang, Y., et al.: Improved YOLOv4-tiny network for real-time electronic component detection[J]. Sci. Rep. 11(1), 22744 (2021). https://doi.org/10.1038/s41598-021-02225-y10.1038/s41598-021-02225-y ↗
50. Yu, Z.: YOLO V5s-based deep learning approach for concrete cracks detection[C]//SHS Web of Conferences. EDP Sciences, 144: 03015. (2022). https://doi.org/10.1051/shsconf/20221440301510.1051/shsconf/202214403015 ↗
51. Zhang, Y., Fang, X., Guo, J., et al.: CURI-YOLOv7: A lightweight YOLOv7tiny target detector for citrus trees from UAV remote sensing imagery based on embedded device[J]. Remote Sens. 15(19), 4647 (2023). https://doi.org/10.3390/rs1519464710.3390/rs15194647 ↗
52. 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[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.: 7464–7475. (2023). https://doi.org/10.48550/arXiv.2207.0269610.48550/arXiv.2207.02696 ↗
53. Ren, S., He, K., Girshick, R., et al.: Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Adv. Neural. Inf. Process. Syst. 28 (2015). https://doi.org/10.48550/arXiv.1506.0149710.48550/arXiv.1506.01497 ↗
54. Kong, Y., Shang, X., Jia, S.: Drone-DETR: Efficient small object detection for remote sensing image using enhanced RT-DETR model[J]. Sensors. 24(17), 5496 (2024). https://doi.org/10.3390/s2417549610.3390/s24175496 ↗
55. Oquab, M., Darcet, T., Moutakanni, T., et al.: Dinov2: Learning robust visual features without supervision[J]. ArXiv Preprint arXiv:2304.07193, 2023. https://doi.org/10.48550/arXiv.2304.0719310.48550/arXiv.2304.07193 ↗
56. Tian, Z., Shen, C., Chen, H., et al.: Fcos: Fully convolutional one-stage object detection[C]//Proceedings of the IEEE/CVF international conference on computer vision.: 9627–9636. (2019). https://doi.org/10.48550/arXiv.1904.0135510.48550/arXiv.1904.01355 ↗
57. Tan, M., Pang, R., Le, Q.V., Efficientdet: Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.: 10781–10790. (2020). https://doi.org/10.48550/arXiv.1911.0907010.48550/arXiv.1911.09070 ↗
58. Mery, D., Riffo, V., Zscherpel, U., et al.: GDXray: The database of X-ray images for nondestructive testing[J]. J. Nondestr. Eval. 34(4), 42 (2015). https://doi.org/10.1007/s10921-015-0315-710.1007/s10921-015-0315-7 ↗