航空兵器2024，Vol.31Issue(1)：97-102,6.DOI:10.12132/ISSN.1673-5048.2023.0127
基于L-FPN的无人机上小目标识别模型轻量化方法
A Lightweight Method for Small Object Detection Models on Unmanned Aerial Vehicles Based on L-FPN
摘要
Abstract
Oriented object detection in remote sensing images is a current research hotspot.Due to the var-ying heights and equipment used in capturing remote sensing images,the ground sampling distance(GSD)of each image also varies,causing many small objects to be easily overlooked.Existing rotated object detection al-gorithms are mainly aimed at multi-scale object detection in general scenarios.The feature pyramid network(FPN)has complex and time-consuming fusion computations,which still faces great challenges when deployed on edge devices like UAVs.Therefore,this paper proposes a lightweight method for small object detection in UAVs based on L-FPN.First,normalize the scale according to the GSD information of the image.Second,re-move redundant high-level feature maps in the FPN.Finally,adjust the anchor box sizes for small object detec-tion.The method is trained and validated on the DOTA dataset.Results show that compared to the traditional models,the proposed L-FPN-based lightweight method for small object detection in UAVs achieves consistent recognition accuracy,with 2.7%fewer model parameters,28%smaller model size,and 13.24%faster infer-ence speed.关键词
目标检测/特征金字塔/模型轻量化/遥感图像/无人机Key words
object detection/feature pyramid/model lightweight/remote sensing images/UAV分类
军事科技引用本文复制引用
魏昊坤,刘敬一,陈金勇,楚博策,孙裕鑫,朱进..基于L-FPN的无人机上小目标识别模型轻量化方法[J].航空兵器,2024,31(1):97-102,6.基金项目
中国博士后科学基金项目(2021M703021) （2021M703021）
河北省重点研发计划项目(22340301D) （22340301D）
河北省博士后基金项目(B2021003031) （B2021003031）
