计算机与现代化Issue(2)：93-99,7.DOI:10.3969/j.issn.1006-2475.2024.02.015
基于轻量化YOLOv4机场场面遥感图像目标检测方法
Lightweight YOLOv4-based Target Detection Method for Remote Sensing Images of Airport Fields
摘要
Abstract
Aiming at the problems that existing remote sensing image target detection methods suffer from the loss of local fea-ture information in deep CNNs and low detection accuracy of complex scenes,a target detection method based on lightweight YOLOv4 is proposed.Firstly,the lightweight neural network Ghostnet is used to replace the cspdarknet53 network used as the backbone feature extraction in YOLOv4.Secondly,to improve the complex environment detection capability,CycleGAN is used to simulate night scenes,and again,the transformer module is fused to make the model easy to capture inter-feature relation-ships and local information of the network.Finally,Adam optimiser and K-means++screening anchor frame are used to accelerate the convergence speed,and the example is validated with RSOD aerial remote sensing dataset.The experimental results show that the MAP value is improved by 6.65 percentage points and the number of parameters is reduced by 84.7%compared with the original YOLOv4,i.e.the algorithm in this paper can meet the real-time target detection of aircraft on the airport field in complex scenes.关键词
实时目标检测/遥感图像/复杂场景/机场场面Key words
real-time target detection/remote sensing image/complicated scene/airport field分类
信息技术与安全科学引用本文复制引用
杨轲,董兵,吴悦,郝宽公,彭自琛..基于轻量化YOLOv4机场场面遥感图像目标检测方法[J].计算机与现代化,2024,(2):93-99,7.基金项目
四川省科技计划项目(2022YFG0197) （2022YFG0197）
民用航空飞行学院重点科研项目(ZJ2021-09) （ZJ2021-09）
