Aiming at the problem that the current drone RF signal identification models have low identification accuracy under the condition of low SNR, and do not support the key parameters such as detection signal duration and bandwidth, this paper proposes a drone radio frequency signal identification method based on YOLOv8n-PEM target detection model. Firstly, the original drone RF signal is downsampled based on discrete wavelet transform, and then the time-frequency characteristics are extracted by short-time Fourier transform. Finally, the signal identification and parameter estimation are completed by using YOLOv8n-PEM model. In terms of the model, the CPF module is designed based on partial convolution to enhance the extraction ability of advanced time-frequency features and improve the robustness of the model. At the same time, the EMA mechanism is introduced to suppress the interference of background noise on the model reasoning. The experimental results show that the YOLOv8n-PEM model has an mAP of 96.08% and an FPS of 107 frames per second under low SNR conditions of -20 to -10 dB, the model parameters are reduced by 38% compared with the baseline model, indicating its value for practical deployment.