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.