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The complex environment of power transmission lines renders foreign object attachment to electrical equipment a frequent cause of faults. However, existing object detection frameworks struggle to accurately identify the types of foreign objects. This manuscript aims to address the scarcity of samples of foreign objects on power transmission lines and develop a high-precision, low-latency detection algorithm. Artificial Intelligence Generated Content (AIGC) is employed to generate high-quality images, providing abundant training data. The newly introduced Spatial and Channel Reconstruction Convolution (SCConv) reduces redundant computations and promotes representative feature learning. Additionally, the reconstructed Efficient Reparameterized Generalized-FPN (Efficient RepGFPN) effectively exchanges high-level semantic information and low-level spatial information without adding extra computational burden, which is advantageous for handling foreign objects of varying sizes. The newly designed Squeeze and Excitation Detect (SE-Detect) enables the extraction of richer feature information with fewer parameters. These enhancements are specifically designed to improve the detection of small and irregular foreign objects under complex background interference, which is a common challenge in aerial inspection scenarios. WIoU with better balance sample quality is selected as the loss function for training. Finally, a distillation schema is introduced to improve performance to a higher level. In conclusion, the improved model achieves a 9% increase in the mAP@.5 and a 9.1% improvement in the recall rate when compared to YOLOv8. Particularly noteworthy is the mAP@.5 of 93.9% in bird's nest detection. These results confirm the algorithm's effectiveness in scenarios involving small targets and cluttered visual environments.