一种用于可见光通信信号调制格式识别的改进YOLOv5s算法

Improved YOLOv5s algorithm for modulation format recognition of visible light communication signal

针对可见光通信信号在传输中易受信道环境和背景噪声干扰等因素影响调制格式识别精度的问题,提出一种用于可见光通信信号调制格式识别的改进YOLOv5s(You Only Look Once)算法。首先,通过YOLOv5s算法网络输入端引入Mixup数据增强方式,将其与原网络中的Mosaic数据增强方式相结合,提升网络的鲁棒性,并增强算法在不同调制格式信号间的泛化能力;其次,将自适应空间特征融合(ASFF)引入到Neck网络中,充分提取不同层次的特征,提高检测精度。实验结果表明,在混合信噪比条件下,所提改进算法的平均精度均值(mAP)达到了0.903,比原始YOLOv5s算法提升了0.7%,且在信噪比为20 dB时mAP高达0.993。

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.