A Method for UAV Object Recognition Based on Improved YOLOv5
According to statistical data,as of 2017,over 3 million UAVs have been sold worldwide.UAVs have characteristics such as small size,low cost,and large quantity,which lead to a series of security issues and pose a serious threat to public safety.The traditional methods for UAV recognition mainly include radar detection and acoustic detection.After analyzing the drawbacks of these traditional recognition methods,this paper proposes an UAV recognition method based on improved YOLOv5.It incorporates the CBAM(Convolutional Block Attention Module)Attention Mechanism based on the original YOLOv5 model,so as to enhance the capability of target feature extraction and improve the performance of the network model.Furthermore,the DeepSORT tracking algorithm can be introduced to provide detection response for UAV's tracking.Through testing on the dataset,the accuracy of the improved model is improved by 5.24%compared to the original model,which meets the recognition requirements basically.
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