电讯技术2025，Vol.65Issue(4)：495-502,8.DOI:10.20079/j.issn.1001-893x.240110002
一种基于YOLOv8网络架构的机场飞鸟检测方法
An Airport Bird Detection Method Based on YOLOv8 Network
摘要
Abstract
To overcome the drawbacks of low accuracy and slow speed in manual bird detection at airports,as well as the high cost associated with radar detection,and ensure the safe operation of civil aviation,deep learning object detection algorithms are used to achieve accurate perception of birds near airports.To enhance the network's focus on crucial features,an Efficient Channel Attention(ECA)attention mechanism is incorporated into the Neck,resulting in a significant improvement in accuracy while adding a small number of parameters.The Muti-branch C3(MBC3)module is designed to strengthen the model's expressive capability by introducing branches with different receptive fields.The impact of different network widths and depths on model performance is explored,and appropriate width and depth factors for the model are selected.The Shallow Feature-Path Aggregation Network(SF-PAN)structure is proposed to address the issue of feature loss in detecting small bird targets.Testing on an airport bird dataset demonstrates that the modified YOLOv8 achieves a mAP@50 of 82.9%,showcasing a 2.4%improvement over the original YOLOv8,while maintaining a speed of 31 frames per second.The improved YOLOv8 meets the requirements for real-time and accurate detection of birds at airports and offeres a new approach to bird detection in complex airport environments.关键词
机场飞鸟检测/鸟击防范/注意力机制/多分支卷积/特征融合Key words
airport bird detection/bird strike prevention/attention mechanism/multi-branch convolution/feature fusion分类
信息技术与安全科学引用本文复制引用
孔建国,张向伟,赵志伟,梁海军..一种基于YOLOv8网络架构的机场飞鸟检测方法[J].电讯技术,2025,65(4):495-502,8.基金项目
国家重点研发计划(2021YFF0603904) （2021YFF0603904）
中央高校基本科研业务费专项资金资助(PHD2023-035,ZHMH2022-009) （PHD2023-035,ZHMH2022-009）
中央高校基本科研业务费资助项目(24CAFUC10192) （24CAFUC10192）
