Improved Target Detection Algorithm for UAV Images with RT-DETR
This paper proposes an improved RT-DETR algorithm for unmanned aerial vehicle(UAV)target detection in light and small-sized UAV image targets.Addressing issues such as low detection accuracy due to the flexible and diverse nature of targets and complex and variable environments,the proposed method enhances the feature extraction capability of the detection model by integrating lightweight SimAM attention and inverted residual modules into the ResNet-r18 backbone network.Furthermore,a cascaded group attention mechanism is employed to optimize the inverted residual modules and feature interaction modules,improving feature selection capability and achieving refined acquisition of target detection information.Additionally,a 160×160 detection layer is introduced in the neck network to enhance the perception capability of small targets during the feature fusion stage.Finally,the experimental results based on the VisDrone2019 dataset show that the improved model has lower number of parameters and higher detection accuracy.Further experiments on the Alver_Lab_Ulastirma and HIT-UAV datasets validate the effectiveness and robustness of the proposed improvements.
small target detectiondetection Transformer(DETR)attention mechanismTransformerresidual link
万方数据
维普
