现代电子技术2024，Vol.47Issue(19)：47-54,8.DOI:10.16652/j.issn.1004-373x.2024.19.008
遥感军事坦克轻量化检测的MSG-YOLOv7算法
Lightweight detection algorithm MSG-YOLOv7 for military tanks in remote sensing images
摘要
Abstract
In view of the large volume and heavy computation of the military tank detection algorithm in remote sensing images,a lightweight remote sensing military tank target detection algorithm MSG-YOLOv7 is proposed.In the algorithm,the MobileNetv3 is taken as the backbone network,and the inverted residual structure and the adaptive scaling method are used to extract features,so that the volume and computation amount of the model are reduced.An SD-MP structure is designed to improve the ability of detailed feature representation,so as to eliminate the small target feature loss caused by downsampling operations.A module named GD-ELAN is devised based on GCNet and depthwise separable convolution.This module enhances the model´s perception of long-distance relationships by global context modeling,capture global information effectively in a lightweight manner and improve the model performance.The experimental results show that the average precision(AP)of MSG-YOLOv7 of the proposed model on the public Google Earth remote sensing military tank dataset reaches 99.02%,with a volume reduction of 60%in comparison with that of the original,a computational complexity of 18.59 GFlops,and an FPS of 41,which proves that the model is applicable to remote sensing military tank detection scenarios that require high performance,high speed and small model volume.关键词
遥感图像/军事坦克检测/YOLOv7/轻量化网络/SD-MP/GD-ELANKey words
remote sensing image/military tank inspection/YOLOv7/lightweight network/SD-MP/GD-ELAN分类
电子信息工程引用本文复制引用
谢国波,吴陈锋,林志毅..遥感军事坦克轻量化检测的MSG-YOLOv7算法[J].现代电子技术,2024,47(19):47-54,8.基金项目
国家自然科学基金资助项目(61802072) （61802072）
南方电网委托课题(GDKJXM20230718) （GDKJXM20230718）
