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Aiming at the problems of small target size, fuzzy edges, and vulnerability to noise and background interference in defect areas of photovoltaic infrared images, an improved algorithm based on YOLOv11 was proposed. Firstly, a guided local-global spatial attention (GLGSA) module is designed to effectively integrate Local salient region information and Global context semantics to improve the discrimination of feature representation. Secondly, the GLGSA module was combined with the bidirectional feature fusion structure BiFPN to construct the GLGSA-BiFPN structure to improve the effect of multi-scale feature fusion. The P2 detection layer was added to enhance the detection ability of minimal targets. Finally, the NWD loss function is introduced to replace the original loss function to enhance the positioning accuracy of small targets. Experimental verification is carried out on the PV-HSD-2025 photovoltaic hot spot data set. The results show that the detection accuracy of the improved algorithm mAP₅₀ and mAP_50-95 is 9.1% and 5.6% higher than that of YOLOv11n. Effectively improve the accuracy of photovoltaic small target defect detection.

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针对光伏红外图像中缺陷区域存在目标尺寸小、边缘模糊、易受噪声与背景干扰等问题，提出一种基于YOLOv11的改进算法。首先设计一种引导式局部-全局空间注意力GLGSA模块，用于有效融合局部显著区域信息与全局上下文语义，提升特征表达的判别性。其次，将GLGSA模块与双向特征融合结构BiFPN结合，构建出GLGSA-BiFPN结构提升多尺度特征融合的效果。新增P2检测层以增强对极小目标的检测能力。最后引入NWD损失函数替换原损失函数，增强对小目标的定位精度。在PV-HSD-2025光伏热斑数据集上进行实验验证，结果表明改进算法的检测精度mAP₅₀和mAP_50-95相比于YOLOv11n分别提高9.1%和5.6%。有效提高光伏小目标缺陷检测的精度。

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牟涛，硕士，主要研究方向为目标检测、机器视觉。E-mail: 1870580536@qq.com

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牟涛，硕士，主要研究方向为目标检测、机器视觉。E-mail: 1870580536@qq.com

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