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Steel defect detection is critical for industrial quality control, yet performance is constrained by multi-scale variations, small targets, and background interference. To enhance the accuracy and efficiency of the detection model, this paper proposes a defect detection network based on an improved version of YOLO11, named LiteSteel-YOLO. First, a Lightweight Multi-Scale Fusion module (C3k2-LMSF) is designed to enhance multi-scale defect perception through fused convolutional kernels and feature guidance mechanisms. Second, a spatial-channel aware upsampling module (SCAM) is proposed, which improves the robustness of small target detection and suppresses noise through channel reorganization and spatial offset operations. Finally, an Efficient-Head detector optimized via structural reconfiguration is introduced to maximize computational efficiency. Experimental results show that the LiteSteel-YOLO receives mAP@50 of 81.7% and 70.7% with inference speed of 338 and 530 FPS on the NEU-DET and GC10-DET datasets (surpassing YOLO11 by 4.0% and 2.3%). The proposed framework enhances the accuracy and efficiency of steel defect detection, providing a solution for industrial inspection scenarios.

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钢材缺陷检测对工业质量管控至关重要，但其多尺度、小目标及背景干扰问题制约检测性能。为了提升模型的检测精度与效率，提出一种基于YOLO11改进的缺陷检测网络LiteSteel-YOLO。首先，设计轻量级多尺度融合结构（C3k2-LMSF），通过多尺度卷积核融合与特征引导机制增强多尺度缺陷感知；其次，提出空间通道感知上采样模块（SCAM），基于通道重组与空间偏移运算机制提升小目标检测的鲁棒性并抑制噪声；最后，构建轻量化高效检测头（Efficient-Head）优化计算效率。实验结果表明，所提出的模型在NEU-DET和GC10-DET数据集上的mAP@50分别达到81.7%和70.7%，相较于原模型YOLO11提升了4.0%和2.3%，检测速度为338和530 FPS，有效地提升了对钢材缺陷的检测精度与效率，为工业检测场景提供了解决思路。

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周迅，硕士研究生，主要研究方向为计算机视觉、缺陷检测。E-mail: zhouxunn_n@163.com

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周迅，硕士研究生，主要研究方向为计算机视觉、缺陷检测。E-mail: zhouxunn_n@163.com

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李帆，硕士研究生，主要研究方向为计算机视觉、缺陷检测。E-mail: lf_2426923409@163.com

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李帆，硕士研究生，主要研究方向为计算机视觉、缺陷检测。E-mail: lf_2426923409@163.com

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