To address the problems of missed detections and low detection accuracy of YOLOv11n in photovoltaic (PV) panel defect detection, this paper proposes an improved YOLOv11n-based PV panel defect detection algorithm. A novel SPPF-LDESKA module is designed to enhance the original spatial pyramid pooling fast (SPPF) module. The module integrates the concepts of lightweight detail-enhanced convolution, which combines model lightweighting and fine-grained feature enhancement—with a large separable kernel attention mechanism to expand the receptive field and effectively strengthen the extraction of small-object information.In the feature pyramid network （FPN） stage, inspired by the context-guided feature modulation (CGFM), an efficient shared convolutional module (ESCM) is designed. This module incorporates the efficient channel attention （ECA） mechanism, employing 1D convolution along the channel dimension to perform local inter-channel interaction. Furthermore, shared convolution is utilized to reduce redundant computation and prevent repetitive learning, thereby decreasing the parameter count and enhancing the adaptive adjustment of contextual information in multi-scale feature fusion.Additionally, a lightweight detection head is introduced, leveraging shared convolution to further reduce model parameters. Experimental results demonstrate that, compared with the original YOLOv11n model, the proposed method reduces computational cost by 6.25%, accuracy improved by 4.5%,while mAP@50 improves from 87.5% to 89.5%, achieving a 2.3% performance gain. These results validate that the improved algorithm achieves superior performance and better adaptability for PV panel defect detection tasks.