An efficient lightweight detection model for steel surface defects with dynamic deformable head

Abstract The accurate detection of steel surface defects remains challenging because of their irregular shapes and complex backgrounds, which often result in missed detections and false positives. Moreover, existing models are unsuitable for edge devices due to large parameters and high computational demands. To address these issues, this paper presents DCDF-YOLO, a lightweight steel surface defect detection model based on YOLOv8n. First, a novel CSPDC feature extraction module replaces the standard C2f module by incorporating dual convolution. Group convolution techniques arrange filters efficiently to optimize information flow and enhance extraction efficiency and representation capacity. Second, a lightweight cross scale feature fusion module named CCFM is introduced during fusion to reduce parameters and computational cost while improving adaptability to scale variations. Third, a Dynamic Deformable Head (DDH) is proposed to improve detection of small defects and integrate feature diversity across scales. This detection head addresses limitations in handling long range dependencies and spatially adaptive aggregation, capturing local details and structural features effectively. Finally, a novel bounding box loss function Focaler-SIoU is introduced. It focuses on regression samples of varying difficulty and incorporates an angular penalty mechanism to enhance precision, inference capability, and robustness in defect recognition. The experimental results demonstrate that the improved model achieves [email protected] gains of 4.5% and 2.7% on the public steel datasets GC10-DET and NEU-DET, respectively, compared to the baseline YOLOv8n. Additionally, the model’s parameter is reduced by 28.6% to 2.15M. Compared with other mainstream object detection models, the DCDF-YOLO model achieves an optimal balance between detection accuracy and lightweight design, meeting the requirements of edge devices operating under limited computational resources.