DGYOLOv8: An Enhanced Model for Steel Surface Defect Detection Based on YOLOv8

被引：0

作者：

Zhu, Guanlin ^{[1
]}

Qi, Honggang ^{[1
]}

Lv, Ke ^{[1
]}

机构：

[1] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China

来源：

MATHEMATICS | 2025年 / 13卷 / 05期

基金：

中国国家自然科学基金;

关键词：

DGYOLOv8; defect detection; deformable convolutional networks; global attention mechanism; InnerMPDIoU; VISION;

D O I：

10.3390/math13050831

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The application of deep learning-based defect detection models significantly reduces the workload of workers and enhances the efficiency of inspections. In this paper, an enhanced YOLOv8 model (DCNv4_C2f + GAM + InnerMPDIoU + YOLOv8, hereafter referred to as DGYOLOv8) is developed to tackle the challenges of object detection in steel surface defect detection tasks. DGYOLOv8 incorporates a deformable convolution C2f (DCNv4_C2f) module into the backbone network to allow adaptive adjustment of the receptive field. Additionally, it integrates a Gate Attention Module (GAM) within the spatial and channel attention mechanisms, enhancing feature selection through a gating mechanism that strengthens key features, thereby improving the model's generalization and interpretability. The InnerMPDIoU, which incorporates the latest Inner concepts, enhances detection accuracy and the ability to handle detailed aspects effectively. This model helps to address the limitations of current networks. Experimental results show improvements in precision (P), recall (R), and mean average precision (mAP) compared to existing models.

引用

页数：16

共 29 条

[1] Fang X., Luo Q., Zhou B., Li C., Tian L., Research progress of automated visual surface defect detection for industrial metal planar materials, Sensors, 20, (2020)
[2] Pan X., Jia N., Mu Y., Gao X., Survey of small object detection, J. Image Graph, 28, pp. 2587-2615, (2023)
[3] Leng J., Mo M., Zhou Y., Ye Y., Gao C., Gao X., Recent advances in drone-view object detection, J. Image Graph, 28, pp. 2563-2586, (2023)
[4] LeCun Y., Bottou L., Bengio Y., Haffner P., Gradient-based learning applied to document recognition, Proc. IEEE, 86, pp. 2278-2324, (1998)
[5] Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., Courville A., Bengio Y., Generative adversarial nets, Commun. ACM, 63, pp. 139-144, (2020)
[6] Bhatt P.M., Malhan R.K., Rajendran P., Shah B.C., Thakar S., Yoon Y.J., Gupta S.K., Image-based surface defect detection using deep learning: A review, J. Comput. Inf. Sci. Eng, 21, (2021)
[7] Redmon J., You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
[8] Zhong H., Wu B., Zhang X., Hu Y., Zhou T., Zhang Y., Steel Surface Defect Detection Based on an Improved YOLOv5 Model, Proceedings of the 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP), pp. 51-55
[9] Lv X., Xu J., Steel Plate Surface Defect Detection Method-Based on Improved YOLO Algorithm, Proceedings of the 2023 China Automation Congress (CAC), pp. 1331-1335
[10] Dehaerne E., Dey B., Esfandiar H., Verstraete L., Suh H.S., Halder S., De Gendt S., Yolov8 for defect inspection of hexagonal directed self-assembly patterns: A data-centric approach, Proceedings of the 38th European Mask and Lithography Conference (EMLC 2023), pp. 286-299, (2023)

← 1 2 3 →