Design of Small Target Detection Algorithm for Aerial Images
In view of the low accuracy of the traditional detection algorithm in aerial image target detection,and the problems of false detection and missed detection,an improved algorithm RBN-YOLOv5 based on YOLOv5 was proposed.A C3RepBlock feature extraction module based on RepVGG was designed to add more discriminative shallow features of the small target detection layer,and a larger receptive field was obtained through the joint representation of local and global information.The BiFormer attention mechanism was introduced to improve the detection accuracy of the model,and the loss function was improved based on the normalized Wasserstein distance to enhance the localization ability of small targets.The training results on the VisDrone2019 dataset show that the detection accuracy of RBN-YOLOv5 is improved by 9.8%compared with the original YOLOv5 model,and the number of model parameters is greatly reduced.
万方数据
维普
