Author(s)

Xinrui Chen ¹, Zijian Dong ¹, Jiangzheng Xu ¹, Zhaojin Huangfu ¹

Affiliation(s)

¹ School of Electronic Engineering, Jiangsu Ocean University, Lianyungang, Jiangsu, 222000, China

Corresponding Author

Zijian Dong

ABSTRACT

The surface defect detection of aluminum profiles is a crucial core link in industrial quality control. Traditional detection methods have practical problems such as easy missed detection of small-target defects and easy misjudgment in complex texture scenarios. This study constructs an efficient detection model suitable for the surface of aluminum profiles based on the YOLOv11 algorithm framework. EfficientViT is used to reconstruct the backbone network, and the multi-scale feature extraction capability is enhanced through hierarchical attention mechanisms and lightweight convolution operations. A Dynamic Deformable Feature Pyramid Network (DDFPN) is introduced, integrating RepGFPN re-parameterized connections, VoVGSCSP grouped convolution, and CoordAtt coordinate attention mechanism to achieve adaptive fusion of defect features and directional sensitivity perception. Experimental results show that compared with the original model, the improved EDHF-YOLO model significantly improves detection accuracy and greatly reduces calculation amount, effectively balancing detection performance and computational efficiency, and providing an innovative technical solution for surface defect detection of aluminum profiles.

KEYWORDS

Defect Detection, EDHF-YOLO, RepGFPN, Dynamic Deformable Feature Pyramid

CITE THIS PAPER

Xinrui Chen, Zijian Dong, Jiangzheng Xu, Zhaojin Huangfu, Algorithm for Surface Defect Detection of Aluminum Profiles Based on Improved YOLOv11. Automation and Machine Learning (2025) Vol. 6: 146-155. DOI: http://dx.doi.org/10.23977/autml.2025.060117.

REFERENCES

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