Steel Surface Defect Detection via the Multiscale Edge Enhancement Method
Yuanyuan Wang1,*, Yemeng Zhu1, Xiuchuan Chen1, Tongtong Yin1, Shiwei Su2
1 College of Computer and Software Engineering, Huaiyin Institute of Technology, Huai’an, 223003, China
2 China Mobile Group Jiangsu Co., Ltd., Huai’an, 223003, China
* Corresponding Author: Yuanyuan Wang. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072404
Received 26 August 2025; Accepted 17 October 2025; Published online 10 November 2025
Abstract
To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects, similar defects and background features, and similarities between different defects, this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network (MSESE), which is built upon the You Only Look Once version 11 nano (YOLOv11n). To address the difficulty of locating defect edges, we first propose an edge enhancement module (EEM), apply it to the process of multiscale feature extraction, and then propose a multiscale edge enhancement module (MSEEM). By obtaining defect features from different scales and enhancing their edge contours, the module uses the dual-domain selection mechanism to effectively focus on the important areas in the image to ensure that the feature images have richer information and clearer contour features. By fusing the squeeze-and-excitation attention mechanism with the EEM, we obtain a lighter module that can enhance the representation of edge features, which is named the edge enhancement module with squeeze-and-excitation attention (EEMSE). This module was subsequently integrated into the detection head. The enhanced detection head achieves improved edge feature enhancement with reduced computational overhead, while effectively adjusting channel-wise importance and further refining feature representation. Experiments on the NEU-DET dataset show that, compared with the original YOLOv11n, the improved model achieves improvements of 4.1% and 2.2% in terms of mAP@0.5 and mAP@0.5:0.95, respectively, and the GFLOPs value decreases from the original value of 6.4 to 6.2. Furthermore, when compared to current mainstream models, Mamba-YOLOT and RTDETR-R34, our method achieves superior performance with 6.5% and 8.9% higher mAP@0.5, respectively, while maintaining a more compact parameter footprint. These results collectively validate the effectiveness and efficiency of our proposed approach.
Keywords
Steel defects; object detection algorithms; small target; multiscale; attention mechanism
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