Liu Yuan, Liu Yilong, Guo Xiaoyan, Ling Xi, Geng Qingyi
School of Mathematics, Northwest University, 1 Xuefu Avenue, Xi'an, 710127, Shaanxi Province, China.
Sci Rep. 2025 Apr 1;15(1):11105. doi: 10.1038/s41598-025-94936-9.
This paper addresses the industrial demand for precision and efficiency in metal surface defect detection by proposing SLF-YOLO, a lightweight object detection model designed for resource-constrained environments. The key innovations of SLF-YOLO include a novel SC_C2f module with a channel gating mechanism to enhance feature representation and regulate information flow, and a newly designed Light-SSF_Neck structure to improve multi-scale feature fusion and morphological feature extraction. Additionally, an improved FIMetal-IoU loss function is introduced to boost generalization performance, particularly for fine-grained and small-target defects. Experimental results demonstrate that SLF-YOLO achieves a mean Average Precision (mAP) of 80.0% on the NEU-DET dataset, outperforming YOLOv8's 75.9%. On the AL10-DET dataset, SLF-YOLO achieves a mAP of 86.8%, striking an effective balance between detection accuracy and computational efficiency without increasing model complexity. Compared to other mainstream models, SLF-YOLO demonstrates strong detection accuracy while maintaining a lightweight architecture, making it highly suitable for industrial applications in metal surface defect detection. The source code is available at https://github.com/zacianfans/SLF-YOLO .
本文提出了SLF-YOLO，一种为资源受限环境设计的轻量级目标检测模型，以满足金属表面缺陷检测中对精度和效率的工业需求。SLF-YOLO的关键创新包括一个带有通道门控机制的新型SC_C2f模块，用于增强特征表示和调节信息流，以及一个新设计的Light-SSF_Neck结构，用于改善多尺度特征融合和形态特征提取。此外，还引入了一种改进的FIMetal-IoU损失函数来提高泛化性能，特别是对于细粒度和小目标缺陷。实验结果表明，SLF-YOLO在NEU-DET数据集上的平均精度均值（mAP）达到80.0%，优于YOLOv8的75.9%。在AL10-DET数据集上，SLF-YOLO的mAP达到86.8%，在不增加模型复杂度的情况下，在检测精度和计算效率之间取得了有效平衡。与其他主流模型相比，SLF-YOLO在保持轻量级架构的同时展现出强大的检测精度，使其非常适合金属表面缺陷检测的工业应用。源代码可在https://github.com/zacianfans/SLF-YOLO获取。
