A Lightweight RT-DETR Model for Metal Surface Defect Detection Using Multi-Scale Network and Additive Attention Mechanism
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 · Surface Roughness and Optical Measurements
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (63)
Show source-provided references
1. Zhang, C., Cui, J., Wu, J., Zhang, X.: Attention mechanism and texture contextual information for steel plate defects detection. J. Intell. Manuf. (2023). https://doi.org/10.1007/s10845-023-02149-610.1007/s10845-023-02149-6 ↗
2. Chen, H., Du, Y., Fu, Y., Zhu, J., Zeng, H.: DCAM-Net: a rapid detection network for strip steel surface defects based on deformable convolution and attention mechanism. IEEE Trans. Instrum. Meas. 72, 1–12 (2023). https://doi.org/10.1109/TIM.2023.323869810.1109/TIM.2023.3238698 ↗
3. Wang, W., Mi, C., Wu, Z., Lu, K., Long, H., Pan, B., Li, D., Zhang, J., Chen, P., Wang, B.: A real-time steel surface defect detection approach with high accuracy. IEEE Trans. Instrum. Meas. 71, 1–10 (2022). https://doi.org/10.1109/TIM.2021.312764810.1109/TIM.2021.3127648 ↗
4. Zhou, M., Wu, T., Xia, Z., He, B., Bing Kong, L., Su, H.: Research progress in deep learning for ceramics surface defect detection. Measurement 242, 115956 (2025). https://doi.org/10.1016/j.measurement.2024.11595610.1016/j.measurement.2024.115956 ↗
5. Lu, M., Sheng, W., Zou, Y., Chen, Y., Chen, Z.: WSS-YOLO: an improved industrial defect detection network for steel surface defects. Measurement 236, 115060 (2024). https://doi.org/10.1016/j.measurement.2024.11506010.1016/j.measurement.2024.115060 ↗
6. Zhou, X., Ren, Z., Zhang, Y., Mi, T., Zhou, S., Jiang, Z.: A shunted-swin transformer for surface defect detection in roller bearings. Measurement 238, 115283 (2024). https://doi.org/10.1016/j.measurement.2024.11528310.1016/j.measurement.2024.115283 ↗
7. Xing, J., Jia, M.: A convolutional neural network-based method for workpiece surface defect detection. Measurement 176, 109185 (2021). https://doi.org/10.1016/j.measurement.2021.10918510.1016/j.measurement.2021.109185 ↗
8. He, Y., Song, K., Meng, Q., Yan, Y.: An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Meas. 69, 1493–1504 (2020). https://doi.org/10.1109/TIM.2019.291540410.1109/TIM.2019.2915404 ↗
9. Huang, Y.-C., Hung, K.-C., Lin, J.-C.: Automated machine learning system for defect detection on cylindrical metal surfaces. Sensors 22, 9783 (2022). https://doi.org/10.3390/s2224978310.3390/s22249783 ↗
10. Zheng, H., Kong, L.X., Nahavandi, S.: Automatic inspection of metallic surface defects using genetic algorithms. J. Mater. Process. Technol. 125, 427–433 (2002). https://doi.org/10.1016/S0924-0136(02)00294-710.1016/S0924-0136(02)00294-7 ↗
11. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks, (2016). http://arxiv.org/abs/1506.01497
12. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You Only Look Once: Unified, real-time object detection, (2016). http://arxiv.org/abs/1506.0264010.1109/CVPR.2016.91 ↗
13. Redmon, J., Farhadi, A.: YOLO9000: Better, Faster, Stronger. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). pp. 6517–6525. IEEE, Honolulu, HI (2017). https://arxiv.org/abs/1612.0824210.1109/CVPR.2017.690 ↗
14. Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement, (2018). http://arxiv.org/abs/1804.02767
15. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: Optimal speed and accuracy of object detection, (2020). https://arxiv.org/abs/2004.10934
16. Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., Wei, X.: YOLOv6: A single-stage object detection framework for industrial applications, (2022). http://arxiv.org/abs/2209.02976
17. Gao, L., Zhang, J., Yang, C., Zhou, Y.: Cas-VSwin transformer: a variant swin transformer for surface-defect detection. Computers in Industry 140, 103689 (2022). https://doi.org/10.1016/j.compind.2022.10368910.1016/j.compind.2022.103689 ↗
18. Zhou, Y., Wu, H., Wang, Y., Liu, X., Zhai, X., Sun, K., Zheng, Z., Tian, C., Zhao, H., Jia, W., Li, T., Zhang, Y.: PIndNet: a pixel-wise industrial defect inspection network using multiple pyramid feature aggregation. Measurement 245, 116639 (2025). https://doi.org/10.1016/j.measurement.2024.11663910.1016/j.measurement.2024.116639 ↗
19. Nguyen, H., Ngo, T.Q., Uyen, H.T.T., Duong, M.K.: Enhanced object recognition from remote sensing images based on hybrid convolution and transformer structure. Earth Sci Inform (2025). https://doi.org/10.1007/s12145-025-01751-x10.1007/s12145-025-01751-x ↗
20. Than, P.M., Ha, C.K., Nguyen, H.: Long-range feature aggregation and occlusion-aware attention for robust autonomous driving detection. SIViP (2025). https://doi.org/10.1007/s11760-025-04290-610.1007/s11760-025-04290-6 ↗
21. Sui, T., Wang, J.: An efficient multiscale enhancement network with attention mechanism for aluminium defect detection. Nondestructive Testing and Evaluation. 1–18 (2024). https://doi.org/10.1080/10589759.2024.241600410.1080/10589759.2024.2416004 ↗
22. Nguyen, H., Nguyen, T.A., Toan, N.D.: Optimizing feature extraction and fusion for high-resolution defect detection in solar cells. Intell. Syst. Appl 24, 200443 (2024). https://doi.org/10.1016/j.iswa.2024.20044310.1016/j.iswa.2024.200443 ↗
23. Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: DETRs Beat YOLOs on Real-time object detection, (2024). http://arxiv.org/abs/2304.0806910.1109/CVPR52733.2024.01605 ↗
24. Li, X., Cai, M., Tan, X., Yin, C., Chen, W., Liu, Z., Wen, J., Han, Y.: An efficient transformer network for detecting multi-scale chicken in complex free-range farming environments via improved RT-DETR. Comput. Electron. Agric. 224, 109160 (2024). https://doi.org/10.1016/j.compag.2024.10916010.1016/j.compag.2024.109160 ↗
25. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM. 60, 84–90 (2017). https://doi.org/10.1145/306538610.1145/3065386 ↗
26. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, (2015). http://arxiv.org/abs/1512.03385
27. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications, (2017). http://arxiv.org/abs/1704.04861
28. Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices, (2017). http://arxiv.org/abs/1707.0108310.1109/CVPR.2018.00716 ↗
29. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: GhostNet: More features from cheap operations, (2020). http://arxiv.org/abs/1911.1190710.1109/CVPR42600.2020.00165 ↗
30. Li, S., Guo, S., Han, Z., Kou, C., Huang, B., Luan, M.: Aluminum surface defect detection method based on a lightweight YOLOv4 network. Sci. Rep. 13, 11077 (2023). https://doi.org/10.1038/s41598-023-38085-x10.1038/s41598-023-38085-x ↗
31. Gao, Y., Li, Z., Wang, Y., Zhu, S.: A Novel YOLOv5_ES based on lightweight small object detection head for PCB surface defect detection. Sci. Rep. 14, 23650 (2024). https://doi.org/10.1038/s41598-024-74368-710.1038/s41598-024-74368-7 ↗
32. Chen, Q., Xiong, Q., Huang, H., Tang, S., Liu, Z.: Research on the construction of an efficient and lightweight online detection method for tiny surface defects through model compression and knowledge distillation. Electronics 13, 253 (2024). https://doi.org/10.3390/electronics1302025310.3390/electronics13020253 ↗
33. Zhu, A., Xie, J., Wang, B., Guo, H., Guo, Z., Wang, J., Xu, L., Zhu, S., Yang, Z.: Lightweight defect detection algorithm of tunnel lining based on knowledge distillation. Sci. Rep. 14, 27178 (2024). https://doi.org/10.1038/s41598-024-77404-810.1038/s41598-024-77404-8 ↗
34. Chen, J., Kao, S., He, H., Zhuo, W., Wen, S., Lee, C.-H., Chan, S.-H.G.: Run, don’t walk: chasing higher FLOPS for faster neural networks, (2023). http://arxiv.org/abs/2303.0366710.1109/CVPR52729.2023.01157 ↗
35. Shaker, A., Maaz, M., Rasheed, H., Khan, S., Yang, M.-H., Khan, F.S.: SwiftFormer: efficient additive attention for transformer-based real-time mobile vision applications. In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 17379–17390. IEEE, Paris, France (2023)10.1109/ICCV51070.2023.01598 ↗
36. 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, (2022). http://arxiv.org/abs/2207.0269610.1109/CVPR52729.2023.00721 ↗
37. Jocher, G., Chaurasia, A., Qiu, J.: YOLO by Ultralytics, https://github.com/ultralytics/ultralytics, (2023)
38. Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: YOLOv10: Real-time end-to-end object detection, (2024). http://arxiv.org/abs/2405.14458
39. Khanam, R., Hussain, M.: YOLOv11: An overview of the key architectural enhancements, (2024). http://arxiv.org/abs/2410.17725
40. Li, Z., Wei, X., Hassaballah, M., Li, Y., Jiang, X.: A deep learning model for steel surface defect detection. Complex Intell. Syst. 10, 885–897 (2024). https://doi.org/10.1007/s40747-023-01180-710.1007/s40747-023-01180-7 ↗
41. Li, X., Xu, C., Li, J., Zhou, X., Li, Y.: Multi-scale sensing and multi-dimensional feature enhancement for surface defect detection of hot-rolled steel strip. Nondestructive Testing and Evaluation (2024). https://doi.org/10.1080/10589759.2024.240844110.1080/10589759.2024.2408441 ↗
42. Ma, X., Deng, X., Kuang, H., Liu, X.: YOLOv7-BA: A metal surface defect detection model based on dynamic sparse sampling and adaptive spatial feature fusion. In: 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). pp. 292–296. IEEE, Chongqing, China (2024). https://doi.org/10.1109/IMCEC59810.2024.1057502210.1109/IMCEC59810.2024.10575022 ↗
43. Lv, X., Duan, F., Jiang, J., Fu, X., Gan, L.: Deep metallic surface defect detection: the new benchmark and detection network. Sensors 20, 1562 (2020). https://doi.org/10.3390/s2006156210.3390/s20061562 ↗
44. Li, Z., Wei, X., Jiang, X.: SSDD-Net: A lightweight and efficient deep learning model for steel surface defect detection. In: Liu, Q., Wang, H., Ma, Z., Zheng, W., Zha, H., Chen, X., Wang, L., and Ji, R. (eds.) Pattern Recognition and Computer Vision. pp. 237–248. Springer Nature Singapore, Singapore (2024). https://doi.org/10.1007/978-981-99-8549-4_2010.1007/978-981-99-8549-4_20 ↗
45. Lv, Z., Lu, Z., Xia, K., Zuo, H., Jia, X., Li, H., Xu, Y.: LAACNet: Lightweight adaptive activation convolution network-based defect detection on polished metal surfaces. Engineering Applications of Artificial Intelligence 133, 108482 (2024). https://doi.org/10.1016/j.engappai.2024.10848210.1016/j.engappai.2024.108482 ↗
46. Zhao, W., Chen, F., Huang, H., Li, D., Cheng, W.: a new steel defect detection algorithm based on deep learning. Comput. Intell. Neurosci. 2021, 5592878 (2021). https://doi.org/10.1155/2021/559287810.1155/2021/5592878 ↗
47. Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y.: EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention, (2023). http://arxiv.org/abs/2305.0702710.1109/CVPR52729.2023.01386 ↗
48. Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I.S., Xie, S.: ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders, (2023). http://arxiv.org/abs/2301.0080810.1109/CVPR52729.2023.01548 ↗
49. Li, Y., Hu, J., Wen, Y., Evangelidis, G., Salahi, K., Wang, Y., Tulyakov, S., Ren, J.: Rethinking Vision Transformers for MobileNet Size and Speed, (2023). http://arxiv.org/abs/2212.0805910.1109/ICCV51070.2023.01549 ↗
50. Wang, A., Chen, H., Lin, Z., Han, J., Ding, G.: RepViT: Revisiting Mobile CNN From ViT Perspective, (2024). http://arxiv.org/abs/2307.0928310.1109/CVPR52733.2024.01506 ↗
51. Li, Y., Li, X., Dai, Y., Hou, Q., Liu, L., Liu, Y., Cheng, M.-M., Yang, J.: LSKNet: A Foundation Lightweight Backbone for Remote Sensing, (2024). http://arxiv.org/abs/2403.11735
52. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, (2021). http://arxiv.org/abs/2103.1403010.1109/ICCV48922.2021.00986 ↗
53. Guo, M.-H., Lu, C.-Z., Liu, Z.-N., Cheng, M.-M., Hu, S.-M.: Visual Attention Network, (2022). http://arxiv.org/abs/2202.09741
54. Peng, Y., Sonka, M., Chen, D.Z.: U-Net V2: Rethinking the skip connections of U-Net for medical image segmentation, (2023). https://arxiv.org/abs/2311.17791
55. Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., Ren, Q.: Slim-neck by GSConv: a lightweight-design for real-time detector architectures. J. Real-Time. Image. Proc. 21, 62 (2024). https://doi.org/10.1007/s11554-024-01436-610.1007/s11554-024-01436-6 ↗
56. Chen, Y., Zhang, C., Chen, B., Huang, Y., Sun, Y., Wang, C., Fu, X., Dai, Y., Qin, F., Peng, Y., Gao, Y.: Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases, (2024). http://arxiv.org/abs/2401.0092610.1016/j.compbiomed.2024.107917 ↗
57. Wang, C.-Y., Yeh, I.-H., Liao, H.-Y.M.: YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information, (2024). http://arxiv.org/abs/2402.1361610.1007/978-3-031-72751-1_1 ↗
58. Tian, R., Jia, M.: DCC-CenterNet: a rapid detection method for steel surface defects. Measurement 187, 110211 (2022). https://doi.org/10.1016/j.measurement.2021.11021110.1016/j.measurement.2021.110211 ↗
59. Zuo, L., Su, S., Fan, S., Li, H., Wen, L., Li, X., Xiong, K.: A new dual-branch network with global information for the surface defect detection on solar PV wafer. IEEE Sensors. J. 24, 9197–9207 (2024). https://doi.org/10.1109/JSEN.2024.335921810.1109/JSEN.2024.3359218 ↗
60. Chan, S., Li, S., Zhang, H., Zhou, X., Mao, J., Hong, F.: Feature optimization-guided high-precision and real-time metal surface defect detection network. Sci. Rep. 14, 31941 (2024). https://doi.org/10.1038/s41598-024-83430-310.1038/s41598-024-83430-3 ↗
61. Muhammad Yasir, S., Ahn, H.: Faster metallic surface defect detection using deep learning with channel shuffling. Comput, Mater. Continua. 75, 1847–1861 (2023). https://doi.org/10.32604/cmc.2023.03569810.32604/cmc.2023.035698 ↗
62. Cheng, Z., Gao, L., Wang, Y., Deng, Z., Tao, Y.: EC-YOLO: effectual detection model for steel strip surface defects based on YOLO-V5. IEEE Access 12, 62765–62778 (2024). https://doi.org/10.1109/ACCESS.2024.339135310.1109/ACCESS.2024.3391353 ↗
63. Liao, L., Song, C., Wu, S., Fu, J.: A novel YOLOv10-based algorithm for accurate steel surface defect detection. Sensors 25, 769 (2025). https://doi.org/10.3390/s2503076910.3390/s25030769 ↗