Effectively detecting subtle surface defects in strip steel is vital for industrial quality assurance; however, most existing approaches fail to strike an optimal balance between accuracy and efficiency, especially under real-time processing constraints. To address these limitations, we propose SGS-YOLO, a lightweight detection network built upon an enhanced YOLOv8 architecture. In this work, we offer three core contributions through our proposed method. First, we incorporate the star operation into YOLOv8’s backbone and C2f modules to reduce the model’s parameters and computational cost. Second, we introduce a Group-Sharing Detection Head (GS-Detect), leveraging Group Normalization and shared convolutions to jointly enhance accuracy and inference efficiency. Third, we propose a novel Self-Concatenated Feature Enhancement (SCFE) mechanism to enrich feature diversity and gradient propagation. Notably, SCFE itself is parameter-free, while the reported overall parameter/FLOPs increase arises from subsequent layers that receive widened inputs after concatenation. Compared with YOLOv8n, SGS-YOLO improves mAP@50 by 2.9%, respectively, on the NEU-DET dataset. With 1.49 M parameters and an inference speed of 130.22 FPS, SGS-YOLO achieves a favorable accuracy-efficiency trade-off. Compared to other mainstream models, SGS-YOLO demonstrates strong detection accuracy while maintaining a lightweight architecture, which satisfies the quasi-real-time requirement in multiscale defect detection task.
References
[1]
Wang, J., Chen, T., Xu, X., Zhao, L., Yuan, D., Du, Y., et al. (2024) An Improved Yolov8 Model for Strip Steel Surface Defect Detection. Applied Sciences, 15, Article 52. https://doi.org/10.3390/app15010052
[2]
Kou, X., Liu, S., Cheng, K. and Qian, Y. (2021) Development of a YOLO-V3-Based Model for Detecting Defects on Steel Strip Surface. Measurement, 182, Article 109454. https://doi.org/10.1016/j.measurement.2021.109454
[3]
Chu, Y., Yu, X. and Rong, X. (2024) A Lightweight Strip Steel Surface Defect Detection Network Based on Improved Yolov8. Sensors, 24, Article 6495. https://doi.org/10.3390/s24196495
[4]
Saklakoglu, N., Bolouri, A., Irizalp, S.G., Baris, F. and Elmas, A. (2021) Effects of Shot Peening and Artificial Surface Defects on Fatigue Properties of 50CrV4 Steel. The International Journal of Advanced Manufacturing Technology, 112, 2961-2970. https://doi.org/10.1007/s00170-020-06532-y
[5]
Zheng, X., Zheng, S., Kong, Y. and Chen, J. (2021) Recent Advances in Surface Defect Inspection of Industrial Products Using Deep Learning Techniques. The International Journal of Advanced Manufacturing Technology, 113, 35-58. https://doi.org/10.1007/s00170-021-06592-8
[6]
Wen, X., Shan, J., He, Y. and Song, K. (2023) Steel Surface Defect Recognition: A Survey. Coatings, 13, Article 17. https://doi.org/10.3390/coatings13010017
[7]
Pan, Y. and Zhang, L. (2022) Dual Attention Deep Learning Network for Automatic Steel Surface Defect Segmentation. Computer-Aided Civil and Infrastructure Engineering, 37, 1468-1487. https://doi.org/10.1111/mice.12792
[8]
Ren, S., He, K., Girshick, R. and Sun, J. (2017) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149. https://doi.org/10.1109/tpami.2016.2577031
[9]
He, K., Gkioxari, G., Dollar, P. and Girshick, R. (2017) Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 22-29 October 2017, 2961-2969. https://doi.org/10.1109/iccv.2017.322
[10]
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016) You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 779-788. https://doi.org/10.1109/cvpr.2016.91
[11]
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:2209.02976.
[12]
Wang, C., Bochkovskiy, A. and Liao, H.M. (2023) YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, 17-24 June 2023, 7464-7475. https://doi.org/10.1109/cvpr52729.2023.00721
[13]
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., et al. (2016) SSD: Single Shot Multibox Detector. In: Leibe, B., Matas, J., Sebe, N. and Welling, M., Eds., Lecture Notes in Computer Science, Springer International Publishing, 21-37. https://doi.org/10.1007/978-3-319-46448-0_2
[14]
Tan, M., Pang, R. and Le, Q.V. (2020) Efficientdet: Scalable and Efficient Object Detection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 13-19 June 2020, 10781-10790. https://doi.org/10.1109/cvpr42600.2020.01079
[15]
Jocher, G., Chaurasia, J., Qiu, A. and Stoken, K. (2023) Ultralytics YOLOv8. GitHub Repository.
[16]
Ameli, Z., Nesheli, S.J. and Landis, E.N. (2024) Deep Learning-Based Steel Bridge Corrosion Segmentation and Condition Rating Using Mask RCNN and YOLOv8. Infrastructures, 9, Article 3. https://doi.org/10.3390/infrastructures9010003
[17]
Lu, Z., Chengao, Z., Lu, L., Yan, Y., Jun, W., Wei, X., et al. (2025) Star-YOLO: A Lightweight and Efficient Model for Weed Detection in Cotton Fields Using Advanced YOLOv8 Improvements. Computers and Electronics in Agriculture, 235, Article 110306. https://doi.org/10.1016/j.compag.2025.110306
[18]
He, Y., Song, K., Meng, Q. and Yan, Y. (2020) An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features. IEEE Transactions on Instrumentation and Measurement, 69, 1493-1504. https://doi.org/10.1109/tim.2019.2915404
[19]
Jain, S., Seth, G., Paruthi, A., Soni, U. and Kumar, G. (2022) Synthetic Data Augmentation for Surface Defect Detection and Classification Using Deep Learning. Journal of Intelligent Manufacturing, 33, 1007-1020. https://doi.org/10.1007/s10845-020-01710-x
[20]
Liu, C. and Cheng, H. (2024) Steel Surface Defect Detection Based on YOLOv8-TLC. Applied Sciences, 14, Article 9708. https://doi.org/10.3390/app14219708
[21]
Li, Z., Tian, X., Liu, X., Liu, Y. and Shi, X. (2022) A Two-Stage Industrial Defect Detection Framework Based on Improved-YOLOv5 and Optimized-Inception-Resnetv2 Models. Applied Sciences, 12, Article 834. https://doi.org/10.3390/app12020834
[22]
Wu, Y., Chen, R., Li, Z., Ye, M. and Dai, M. (2024) SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects. Metals, 14, Article 650. https://doi.org/10.3390/met14060650
[23]
Wei, M., Chen, B., Liu, J., Yuan, N., Liu, J. and Ji, Z. (2024) AEDN-YOLO: An Efficient One-Stage Detection Network for Strip Steel Surface Defects. Engineering Research Express, 6, Article 035415. https://doi.org/10.1088/2631-8695/ad681d
[24]
Kong, S., Kong, Y., Chi, X., Feng, X. and Ma, L. (2025) A TBD-YOLO-Based Surface Defect Detection Method for Hot Rolled Steel Strips. Russian Journal of Nondestructive Testing, 61, 137-149. https://doi.org/10.1134/s1061830924603192
[25]
Liu, R., Huang, M., Gao, Z., Cao, Z. and Cao, P. (2023) MSC-DNet: An Efficient Detector with Multi-Scale Context for Defect Detection on Strip Steel Surface. Measurement, 209, Article 112467. https://doi.org/10.1016/j.measurement.2023.112467
[26]
Yang, S., Wang, X., Qian, X., Yu, Y. and Jin, W. (2023) Trident-LK Net: A Lightweight Trident Structure Network with Large Kernel for Muti-Scale Defect Detection. IEEE Access, 11, 131073-131080. https://doi.org/10.1109/access.2023.3333918
[27]
Wen, Y. and Wang, L. (2024) YOLO-SD: Simulated Feature Fusion for Few-Shot Industrial Defect Detection Based on YOLOv8 and Stable Diffusion. International Journal of Machine Learning and Cybernetics, 15, 4589-4601. https://doi.org/10.1007/s13042-024-02175-7
[28]
Liu, Y., Zhao, Y., Zhang, X., Wang, X., Lian, C., Li, J., et al. (2023) Mobilesam-Track: Lightweight One-Shot Tracking and Segmentation of Small Objects on Edge Devices. Remote Sensing, 15, Article 5665. https://doi.org/10.3390/rs15245665
[29]
Zhao, Y., Ju, Z., Sun, T., Dong, F., Li, J., Yang, R., et al. (2023) TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism. Drones, 7, Article 446. https://doi.org/10.3390/drones7070446
[30]
Ma, X., Dai, X., Bai, Y., Wang, Y. and Fu, Y. (2024) Rewrite the Stars. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 16-22 June 2024, 5694-5703. https://doi.org/10.1109/cvpr52733.2024.00544
[31]
Tian, Z., Shen, C., Chen, H. and He, T. (2019) FCOS: Fully Convolutional One-Stage Object Detection. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, 27 October 2019-2 November 2019, 9626-9635. https://doi.org/10.1109/iccv.2019.00972
[32]
Wu, Y. and He, K. (2018) Group Normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C. and Weiss, Y., Eds., Lecture Notes in Computer Science, Springer International Publishing, 3-19. https://doi.org/10.1007/978-3-030-01261-8_1
[33]
Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K.Q. (2017) Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 4700-4708. https://doi.org/10.1109/cvpr.2017.243
Wang, C., Yeh, I. and Mark Liao, H. (2024) YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T. and Varol, G., Eds., Lecture Notes in Computer Science, Springer, 1-21. https://doi.org/10.1007/978-3-031-72751-1_1
[36]
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J. and Ding, G. (2024) YOLOv10: Real-Time End-to-End Object Detection. arXiv:2405.14458.
[37]
Jocher, G., Chaurasia, J., Qiu, A. and Stoken, K. (2024) Ultralytics YOLOv11. GitHub repository.
[38]
Jocher, G., Chaurasia, J., Qiu, A. and Stoken, K. (2025) Ultralytics YOLOv12. GitHub repository.
[39]
Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., et al. (2024) DETRs Beat Yolos on Real-Time Object Detection. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, 16-22 June 2024, 16965-16974. https://doi.org/10.1109/cvpr52733.2024.01605
