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Abstract With the advancement of deep learning technologies, industrial intelligent detection algorithms are gradually being applied in practical steel surface defect detection. Addressing the issues of high computational resource consumption and poor detection performance faced by existing models in large-scale industrial production lines, this paper proposes an EFS-YOLO (Efficient-Fast-Shared-YOLO) model based on improved YOLOv8s architecture. Firstly, the EfficientViT is employed as the feature extraction network, effectively reducing the model’s parameters and calculations while enhancing its capability to represent defect features. Secondly, the designed lightweight C2f-Faster-EffectiveSE Block (CFE-Block) was integrated into the model neck, accelerating feature fusion and better preserving detailed defect features at lower levels. Finally, the model detection head was reconstructed using the concept of shared parameters to address the high computational cost of the original detection head. Experimental results on the NEU-DET and GC10-DET datasets demonstrate that compared to the baseline model, the proposed model achieves a reduction in parameters, calculations and size by 49.5%, 62.7% and 46.9% respectively. It also exhibits an improvement in accuracy by 2.4% and 2.3% on the two datasets. The EFS-YOLO model effectively enhances precision in steel surface defect detection while maintaining lightweight characteristics, offering superior performance.
Chen et al. (Wed,) studied this question.