The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles(UAV)has emerged as a prominent research focus.Due to the considerable distance between UAVs and the photograp...The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles(UAV)has emerged as a prominent research focus.Due to the considerable distance between UAVs and the photographed objects,coupled with complex shooting environments,existing models often struggle to achieve accurate real-time target detection.In this paper,a You Only Look Once v8(YOLOv8)model is modified from four aspects:the detection head,the up-sampling module,the feature extraction module,and the parameter optimization of positive sample screening,and the YOLO-S3DT model is proposed to improve the performance of the model for detecting small targets in aerial images.Experimental results show that all detection indexes of the proposed model are significantly improved without increasing the number of model parameters and with the limited growth of computation.Moreover,this model also has the best performance compared to other detecting models,demonstrating its advancement within this category of tasks.展开更多
When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,includin...When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,including high vulnerability to target occlusion and shape variations,as well as pronounced false alarms and missed detections in low signal-to-noise ratio(SNR)envi-ronments.To address these issues,this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network.The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data.Additionally,the key parameters of the algorithm are optimized through a process of selection and comparison,thereby improving the algorithm's precision.The experimental results show that the improved algo-rithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions,effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.展开更多
In this paper,the detection capabilities and system performance of an energy harvesting(EH)Internet of Things(Io T)architecture in the presence of an unmanned aerial vehicle(UAV)eavesdropper(UE)are investigated.The co...In this paper,the detection capabilities and system performance of an energy harvesting(EH)Internet of Things(Io T)architecture in the presence of an unmanned aerial vehicle(UAV)eavesdropper(UE)are investigated.The communication protocol is divided into two phases.In the first phase,a UAV relay(UR)cooperates with a friendly UAV jammer(UJ)to detect the UE,and the UR and UJ harvest energy from a power beacon(PB).In the second phase,a ground base station(GBS)sends a confidential signal to the UR using non-orthogonal multiple access(NOMA);the UR then uses its harvested energy to forward this confidential signal to IoT destinations(IDs)using the decode-and-forward(DF)technique.Simultaneously,the UJ uses its harvested energy to emit an artificial signal to combat the detected UE.A closed-form expression for the probability of detecting the UE(the detection probability,DP)is derived to analyze the detection performance.Furthermore,the intercept probability(IP)and throughput of the considered IoT architecture are determined.Accordingly,we identify the optimal altitudes for the UR and UJ to enhance the system and secrecy performance.Monte Carlo simulations are employed to verify our approach.展开更多
文摘The application of deep learning for target detection in aerial images captured by Unmanned Aerial Vehicles(UAV)has emerged as a prominent research focus.Due to the considerable distance between UAVs and the photographed objects,coupled with complex shooting environments,existing models often struggle to achieve accurate real-time target detection.In this paper,a You Only Look Once v8(YOLOv8)model is modified from four aspects:the detection head,the up-sampling module,the feature extraction module,and the parameter optimization of positive sample screening,and the YOLO-S3DT model is proposed to improve the performance of the model for detecting small targets in aerial images.Experimental results show that all detection indexes of the proposed model are significantly improved without increasing the number of model parameters and with the limited growth of computation.Moreover,this model also has the best performance compared to other detecting models,demonstrating its advancement within this category of tasks.
基金supported in part by National Natural Science Founda-tion of China(No.62372284)in part by Shanghai Nat-ural Science Foundation(No.24ZR1421800).
文摘When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,including high vulnerability to target occlusion and shape variations,as well as pronounced false alarms and missed detections in low signal-to-noise ratio(SNR)envi-ronments.To address these issues,this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network.The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data.Additionally,the key parameters of the algorithm are optimized through a process of selection and comparison,thereby improving the algorithm's precision.The experimental results show that the improved algo-rithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions,effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.
基金supported in part by Thailand Science Research and Innovation(TSRI)National Research Council of Thailand(NRCT)via International Research Network Program(IRN61W0006)by Khon Kaen University,Thailand。
文摘In this paper,the detection capabilities and system performance of an energy harvesting(EH)Internet of Things(Io T)architecture in the presence of an unmanned aerial vehicle(UAV)eavesdropper(UE)are investigated.The communication protocol is divided into two phases.In the first phase,a UAV relay(UR)cooperates with a friendly UAV jammer(UJ)to detect the UE,and the UR and UJ harvest energy from a power beacon(PB).In the second phase,a ground base station(GBS)sends a confidential signal to the UR using non-orthogonal multiple access(NOMA);the UR then uses its harvested energy to forward this confidential signal to IoT destinations(IDs)using the decode-and-forward(DF)technique.Simultaneously,the UJ uses its harvested energy to emit an artificial signal to combat the detected UE.A closed-form expression for the probability of detecting the UE(the detection probability,DP)is derived to analyze the detection performance.Furthermore,the intercept probability(IP)and throughput of the considered IoT architecture are determined.Accordingly,we identify the optimal altitudes for the UR and UJ to enhance the system and secrecy performance.Monte Carlo simulations are employed to verify our approach.
