一种用于可见光通信信号调制格式识别的改进YOLOv5s算法OA北大核心

Aiming at the problem that modulation format recognition accuracy is susceptible to factors such as channel environ-ment and background noise interference in visible light communication signal transmission,this paper proposes an improved YOLOv5s(You Only Look Once)algorithm for modulation format recognition of visible light communication signals.Firstly,the Mixup data augmentation method is introduced at the input end of the YOLOv5s algorithm network,and it is combined with the Mosaic data augmentation method in the original network to enhance the robustness of the network and improve the generaliza-tion ability of the algorithm among different modulation format signals.Secondly,adaptively spatial feature fusion(ASFF)is in-troduced into the Neck network to fully extract features from different levels and improve detection accuracy.The experimental results indicate that under mixed signal-to-noise ratio conditions,the mean average precision(mAP)of the proposed improved al-gorithm reaches 0.903,representing a 0.7%improvement compared to the original YOLOv5s algorithm.Furthermore,the mAP reaches a high of 0.993 when the signal-to-noise ratio is 20 dB.