Open Access

ARTICLE

Research on Steel Surface Defect Detection Algorithm Based on YOLOv11-ODF

Zhengxiang Ma^1,2,*, Xiaofei Ma¹, Xiaoliang Liu³, Heng Zhang⁴, Weichao Yu¹

1 School of Electronics and Information, Zhengzhou University of Aeronautics, Zhengzhou, China
2 National and Local Joint Engineering Research Center for Intelligent Building Internet of Things Technology and Application, Zhengzhou, China
3 Engineering Technology Research Center, Tianzhu Science And Technology Co., Ltd., Zhengzhou, China
4 Army Artillery and Air Defense Academy, 24 Jianshe East Road, Erqi District, Zhengzhou, China

* Corresponding Author: Zhengxiang Ma. Email:

Structural Durability & Health Monitoring 2026, 20(4), 21 https://doi.org/10.32604/sdhm.2026.079896

Received 30 January 2026; Accepted 24 March 2026; Issue published 30 June 2026

Abstract

Steel surface defect detection is a key technology for ensuring the quality of steel products and the automation of production. However, in actual industrial scenarios, the complex texture background of steel surfaces often leads to low recognition of tiny defect features and easy confusion, and the full extraction and fusion of multi-scale features remain challenging. To address these issues, this paper proposes a lightweight and high-precision detection model based on the improved YOLOv11n, named YOLOv11-ODF. Firstly, in the backbone network, a C3k2_ODConv module integrating full-dimensional convolution (ODConv) is constructed, which enhances the model’s ability to capture subtle defect features through multi-dimensional dynamic weights, and combines the C2PSA attention mechanism to optimize the feature representation in both spatial and channel dimensions. Secondly, in the feature fusion network (Head), an OD_WT_Fuse module is designed to replace the traditional fusion method, effectively improving the efficiency of cross-scale information transmission and semantic consistency. In addition, an anisotropic strip spatial pyramid pooling (ASSPPF) module is designed to further expand the receptive field and enhance the robustness of detecting irregular multi-scale defects. Experimental results show that on the NEU-DET dataset, the mAP@0.5 of YOLOv11-ODF reaches 77.1%, significantly improving by 3.2% compared to the original YOLOv11 model; the precision and recall increase by 1.6% and 4.6%, respectively, significantly reducing the missed detection rate of tiny defects. While achieving significant performance improvements, the model parameters only increase by 0.9 M, achieving an excellent balance between detection accuracy and computational efficiency, providing an effective technical solution for high-quality real-time automatic detection in industrial environments.

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