An aerial image detection method based on improved YOLOv5
Due to issues such as occlusion and overlapping in aerial images,it is challenging for models to achieve stable recogni-tion,which reduces efficiency in areas such as military target tracking,traffic monitoring,and disaster observation.To address these problems,a method based on improved YOLOv5 for aerial image detection has been proposed.This method introduces a new convolu-tional neural network module(Space-to-depth Convolution,SPD-Conv)for low-resolution images and small objects,a small object de-tection head,a soft non-maximum suppression algorithm(Soft Non-maximum Suppression,Soft-NMS),and a regression loss function.Extensive experiments have been conducted on the VisDrone2019 dataset.The experimental results show that the proposed method achieves an average accuracy improvement of 12.5%and a 9.3%increase in mAP@0.5:0.95 metric on the VisDrone2019 dataset.
Small target detectionSPD-ConvSoft-NMSRegression loss function
国家科技期刊平台
NSTL
万方数据
维普
