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Metal surface defect detection using SLF-YOLO enhanced YOLOv8 model.

Yuan Liu¹, Yilong Liu¹, Xiaoyan Guo²

¹School of Mathematics, Northwest University, 1 Xuefu Avenue, Xi'an, 710127, Shaanxi Province, China.

Scientific Reports

|April 1, 2025

Summary

SLF-YOLO, a lightweight object detection model, enhances metal surface defect detection. It achieves high accuracy and efficiency, outperforming existing models in resource-constrained industrial settings.

Keywords:

Lightweight model Multi-scale fusion Optimized loss function Surface defect detection YOLOv8

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Area of Science:

Computer Vision
Machine Learning
Materials Science

Background:

Industrial demand for precise and efficient metal surface defect detection is high.
Existing models often struggle with resource constraints or fine-grained defect identification.
Automated defect detection is crucial for quality control in manufacturing.

Purpose of the Study:

To propose SLF-YOLO, a lightweight object detection model for metal surface defect detection.
To improve feature representation, multi-scale fusion, and loss function for enhanced detection.
To achieve a balance between accuracy and computational efficiency in industrial environments.

Main Methods:

Developed a novel SC_C2f module with channel gating for feature enhancement.
Designed a Light-SSF_Neck structure for improved multi-scale feature fusion and morphological extraction.
Introduced an improved FIMetal-IoU loss function to enhance generalization for small defects.

Main Results:

SLF-YOLO achieved 80.0% mAP on NEU-DET, surpassing YOLOv8 (75.9%).
On AL10-DET, SLF-YOLO reached 86.8% mAP, demonstrating superior performance.
The model maintains a lightweight architecture without compromising detection accuracy.

Conclusions:

SLF-YOLO offers a highly accurate and computationally efficient solution for industrial metal surface defect detection.
The proposed model effectively addresses the challenges of resource-constrained environments and fine-grained defect identification.
SLF-YOLO presents a viable alternative to mainstream models for practical industrial applications.