山东农业大学学报（自然科学版）2024，Vol.55Issue(6)：814-825,12.DOI:10.3969/j.issn.1000-2324.2024.06.001
基于自适应特征融合的无人机小目标检测算法
Uav Small Target Detection Algorithm Based on Adaptive Feature Fusion
摘要
Abstract
This paper proposes a target detection algorithm for images captured by drones,aiming to solve the issues of serious missed detection and low detection accuracy for small objects from a drone's perspective.The main improvements include redesigning the clustering algorithm to generate more accurate prior boxes,introducing an adaptive feature fusion module to enable the model to more flexibly learn contextual feature information,and modifying the detection head to perform object detection on larger feature maps while decoupling classification and regression tasks.Through extensive experiments on the VisDrone2019 dataset,the improved YOLOv5s model showed a 5.8%increase in mAP50 over the baseline model and maintained a high frame rate(67 FPS).The experimental results demonstrate that the proposed improvements significantly enhance the model's detection performance,making it suitable for complex scenarios captured by drones.关键词
小目标检测/检测头/特征融合/聚类算法Key words
Small target detection/detection head/feature fusion/clustering algorithm分类
信息技术与安全科学引用本文复制引用
赵滨淋,陈功,李胜..基于自适应特征融合的无人机小目标检测算法[J].山东农业大学学报（自然科学版）,2024,55(6):814-825,12.基金项目
一类高维投资再保险随机控制问题的神经网络算法研究(2023STY57) （2023STY57）
保险精算中一类随机微分博弈问题及算法研究(KYON202225) （KYON202225）
