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...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。展开更多
Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems,and road target detection is one of the most difficult tasks in the field of computer vision.The challenge ...Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems,and road target detection is one of the most difficult tasks in the field of computer vision.The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments.However,because road targets are prone to complicated backgrounds and sparse features,it is challenging to detect and identify vehicle kinds fast and reliably.We suggest a new vehicle detection model called MEB-YOLO,which combines Mosaic and MixUp data augmentation,Efficient Channel Attention(ECA)attention mechanism,Bidirectional Feature Pyramid Network(BiFPN)with You Only Look Once(YOLO)model,to overcome this problem.Four sections make up this model:Input,Backbone,Neck,and Prediction.First,to improve the detection dataset and strengthen the network,MixUp and Mosaic data improvement are used during the picture processing step.Second,an attention mechanism is introduced to the backbone network,which is Cross Stage Par-tial Darknet(CSPDarknet),to reduce the influence of irrelevant features in images.Third,to achieve more sophisticated feature fusion without increasing computing cost,the BiFPN structure is utilized to build the Neck network of the model.The final prediction results are then obtained using Decoupled Head.Experiments demonstrate that the proposed model outperforms several already available detection methods and delivers good detection results on the University at Albany DEtection and TRACking(UA-DETRAC)public dataset.It also enables effective vehicle detection on real traffic monitoring data.As a result,this technique is efficient for detecting road targets.展开更多
文摘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。
基金funded by the National Natural Science Foundation of China(NSFC)(No.61170110)Zhejiang Provincial Natural Science Foundation of China(LY13F020043).
文摘Rapid and precise vehicle recognition and classification are essential for intelligent transportation systems,and road target detection is one of the most difficult tasks in the field of computer vision.The challenge in real-time road target detection is the ability to properly pinpoint relatively small vehicles in complicated environments.However,because road targets are prone to complicated backgrounds and sparse features,it is challenging to detect and identify vehicle kinds fast and reliably.We suggest a new vehicle detection model called MEB-YOLO,which combines Mosaic and MixUp data augmentation,Efficient Channel Attention(ECA)attention mechanism,Bidirectional Feature Pyramid Network(BiFPN)with You Only Look Once(YOLO)model,to overcome this problem.Four sections make up this model:Input,Backbone,Neck,and Prediction.First,to improve the detection dataset and strengthen the network,MixUp and Mosaic data improvement are used during the picture processing step.Second,an attention mechanism is introduced to the backbone network,which is Cross Stage Par-tial Darknet(CSPDarknet),to reduce the influence of irrelevant features in images.Third,to achieve more sophisticated feature fusion without increasing computing cost,the BiFPN structure is utilized to build the Neck network of the model.The final prediction results are then obtained using Decoupled Head.Experiments demonstrate that the proposed model outperforms several already available detection methods and delivers good detection results on the University at Albany DEtection and TRACking(UA-DETRAC)public dataset.It also enables effective vehicle detection on real traffic monitoring data.As a result,this technique is efficient for detecting road targets.
