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Article

Improved of YOLOv8-n Algorithm for Steel Surface Defect Detection

1
College of Elite Engineer, Changsha University of Science and Technology, Changsha 410114, China
2
College of Mechanical and Vehicle Engineering, Changsha University of Science and Technology, Changsha 410114, China
*
Author to whom correspondence should be addressed.
Metals 2025, 15(8), 843; https://doi.org/10.3390/met15080843
Submission received: 3 July 2025 / Revised: 21 July 2025 / Accepted: 25 July 2025 / Published: 28 July 2025
(This article belongs to the Special Issue Nondestructive Testing Methods for Metallic Material)

Abstract

To address the limitations in multi-scale feature processing and illumination sensitivity of existing steel surface defect detection algorithms, we proposed ADP-YOLOv8-n, enhancing accuracy and computational efficiency through advanced feature fusion and optimized network architecture. Firstly, an adaptive weighted down-sampling (ADSConv) module was proposed, which improves detector adaptability to diverse defects via the weighted fusion of down-sampled feature maps. Next, the C2f_DWR module was proposed, integrating optimized C2F architecture with a streamlined DWR design to enhance feature extraction efficiency while reducing computational complexity. Then, a Multi-Scale-Focus Diffusion Pyramid was designed to adaptively handle multi-scale object detection by dynamically adjusting feature fusion, thus reducing feature redundancy and information loss while maintaining a balance between detailed and global information. Experiments demonstrate that the proposed ADP-YOLOv8-n detection algorithm achieves superior performance, effectively balancing detection accuracy, inference speed, and model compactness.
Keywords: surface defect detection; adaptive weight; receptive field; nondestructive testing surface defect detection; adaptive weight; receptive field; nondestructive testing

Share and Cite

MDPI and ACS Style

Xiang, Q.; Wu, G.; Liu, Z.; Zeng, X. Improved of YOLOv8-n Algorithm for Steel Surface Defect Detection. Metals 2025, 15, 843. https://doi.org/10.3390/met15080843

AMA Style

Xiang Q, Wu G, Liu Z, Zeng X. Improved of YOLOv8-n Algorithm for Steel Surface Defect Detection. Metals. 2025; 15(8):843. https://doi.org/10.3390/met15080843

Chicago/Turabian Style

Xiang, Qingqing, Gang Wu, Zhiqiang Liu, and Xudong Zeng. 2025. "Improved of YOLOv8-n Algorithm for Steel Surface Defect Detection" Metals 15, no. 8: 843. https://doi.org/10.3390/met15080843

APA Style

Xiang, Q., Wu, G., Liu, Z., & Zeng, X. (2025). Improved of YOLOv8-n Algorithm for Steel Surface Defect Detection. Metals, 15(8), 843. https://doi.org/10.3390/met15080843

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