航空兵器2025，Vol.32Issue(3)：91-101,11.DOI:10.12132/ISSN.1673-5048.2024.0163
改进YOLOv8的无人机航拍图像目标检测方法
Improved YOLOv8 Object Detection Method for Drone Aerial Images
摘要
Abstract
A new improved YOLOv8 drone aerial image object detection method,referred to as the BDI-YOLO model,is proposed to address the problems of small target object size and blurry feature information in drone aerial ima-ges,which can lead to missed and false detections.Firstly,the Bidirectional Feature Pyramid Network(BiFPN)is in-troduced to improve the neck structure,utilizing a bidirectional information transmission mechanism and an adaptive feature selection mechanism to enhance the model's ability to extract features of different scales in aerial images.Sec-ondly,replace the detection head with a Dynamic Head(Dyhead)to enhance the model's receptive field for distant small targets,thereby reducing the missed rate and false detection rate.Finally,the Inner-IoU is introduced into the o-riginal CIoU loss function and optimized into the Inner-CIoU loss function,which enhances the assessment of prediction bounding boxes and improves the model's localization precision.The experimental results on the VisDrone2019 dataset show:compared with the YOLOv8 model,the BDI-YOLO model in accuracy mAP@50 and mAP@50:95 has increased by 3.8％ and 2.7％ respectively,with a 4％ increase in recall,a9.4％ decrease in computational complexity,and a 28.8％ decrease in parameter count.The BDI-YOLO model can adapt well to the target detection task of unmanned aerial vehicle aerial images in complex scenes.关键词
目标检测/无人机/航拍图像/YOLOv8/特征提取/动态检测头Key words
object detection/UAV/aerial images/YOLOv8/feature extraction/dynamic head分类
军事科技引用本文复制引用
钟帅,王丽萍..改进YOLOv8的无人机航拍图像目标检测方法[J].航空兵器,2025,32(3):91-101,11.基金项目
中国人民公安大学刑事科学技术双一流创新研究专项(2023SYL06) （2023SYL06）
