Given the challenges posed by photovoltaic component damage detection and the high demands placed on human and computational resources by existing detection technologies, an improved lightweight model named YOLOv8-DM was proposed on the basis of the YOLOv8n.The integration ofelectroluminescence imaging with object detection methods was implemented to achieve photovoltaic defect detection. Innovative components were introduced, including a dynamic scale feature pyramid network and an inverted residual multiscale attention mechanism, along with a Ghost module enhanced by dynamic convolution. These modifications were specifically designed to address the deficiencies observed in the YOLOv8n model regarding feature representation and multiscale object recognition, which enhanced fine-grained detection capabilities and reduced computational complexity. When evaluated on the augmented PVEL-AD dataset, the model demonstrated an improvement of 3% in recall rate and 3.3% in mAP50 compared to the baseline model, with a 34% reduction in parameter count and a 20% decrease in computational demand. The optimized architecture was validated to effectively meet the practical requirements for high-accuracy photovoltaic defect detection with lower computational costs.