Aiming at the current situation that the existing target detection method has low accuracy and the model is too large in the surface defect detection of photovoltaic array, it is difficult to apply to the lightweight UAV detection equipment. An improved lightweight YOLOv8 model is proposed. The use of CSPHet module ensures the ability to extract features while reducing model parameters and improving operating efficiency. The PSA attention mechanism is introduced to integrate the global information into the feature map, which improves the network’s ability to distinguish the target and background, and reduces the influence of noise on target location and classification. The CCFM neck structure is adopted, and the complexity of the model is reduced by adjusting the number of output channels of the model, so as to achieve a more efficient and lightweight network architecture. The global sensing module ACmix is added to enhance the global sensing ability of the model, reduce the interference of irrelevant information, and improve the robustness of the model. The experimental results show that the parameters of the improved YOLOv8 model are reduced by 37%, the calculation amount is reduced by 27%, and the detection accuracy mAP@0.5 is increased to 81.2%. The parameter quantity and calculation amount are significantly reduced, and the detection accuracy is improved while achieving lightweight. Compared with other models, it is more suitable for deployment on lightweight UAV equipment for target detection of surface defects of photovoltaic arrays.