计算机与数字工程2024，Vol.52Issue(10)：2863-2868,2896,7.DOI:10.3969/j.issn.1672-9722.2024.10.002
基于改进YOLOv4的遥感目标检测算法
A Remote Sensing Target Detection Algorithm Based on Improved YOLOV4
摘要
Abstract
Aiming at the multi-scale,diversity and complex background of remote sensing targets,in order to improve the de-tection speed and average accuracy of YOLOv4 algorithm,a new algorithm for remote sensing object detection based on YOLOv4 model is proposed.Firstly,the backbone feature extraction network of YOLOv4 is replaced with Mobilenetv2 to reduce the number of parameters and improve the detection speed.Secondly,novel attention mechanism CoordAttention modules embedded in Mobile-netv2's residual network capture information on sense of orientation and position perception,accurately locate and identify targets of interest.Finally,based on the idea of Inception,an improved RFB module is added to the neck feature enhancement network to en-hance the receptive field and enhance the feature fusion capability of the network.It is shown that the proposed MCR-YOLOv4(Mo-bilenetv2-CoordAttention-RFB-You Only Look Once)algorithm reduces the model size in 45.27 M,1.03%improvement in aver-age accuracy and 56 frames/s compared to the original YOLOv4 algorithm,and is more suitable for the detection of complex remote sensing targets.关键词
数字图像处理/Mobilenetv2/遥感目标/目标检测/YOLOv4Key words
digital image processing/Mobilenetv2/remote sensing target/target detection/YOLOv4分类
天文与地球科学引用本文复制引用
张立夏,马致明,刘战东,彭相澍..基于改进YOLOv4的遥感目标检测算法[J].计算机与数字工程,2024,52(10):2863-2868,2896,7.基金项目
国家自然科学基金项目(编号:62162061)资助. （编号:62162061）
