红外技术2026，Vol.48Issue(5)：554-561,8.
基于DBB-YOLOv10s的无人机小目标检测
Small Object Detection for UAVs Based on DBB-YOLOv10s
摘要
Abstract
To address the challenges of complex background interference and multiscale target detection in UAV-based small object detection tasks,this study proposes a network model based on DBB-YOLOv10s.Using YOLOv10s as the baseline model,the proposed network incorporates dilated convolution(DC2f)to expand the receptive field,a bidirectional feature pyramid network(BiFPN)to achieve multiscale feature fusion,and a BAM attention mechanism to enhance the model's focus on target regions.Experimental results on the VisDrone2021 dataset demonstrate that the proposed algorithm achieves strong performance,with a mean average precision(mAP)of 41.8%and an inference speed of 148 FPS,while maintaining a model size of 6.59 M parameters,ensuring strong practicality.Compared with existing YOLO models and their variants,the proposed model not only maintains high detection accuracy in complex scenarios but also balances real-time performance and computational efficiency,making it suitable for deployment in embedded systems and real-time UAV monitoring tasks.The results indicate that the proposed algorithm significantly enhances detection capability and generalization performance in complex environments.关键词
感受野/多尺度特征/无人机/小目标/YOLOv10Key words
receptive field/multi-scale features/UAV/small object/YOLOv10分类
信息技术与安全科学引用本文复制引用
杨海涛,赵俊羽,王瑞,王华朋,向裕婧,鄢喜爱,孙展明,熊英灼..基于DBB-YOLOv10s的无人机小目标检测[J].红外技术,2026,48(5):554-561,8.基金项目
湖南省重点研发计划(2024AQ2023,2024AQ2024) （2024AQ2023,2024AQ2024）
湖南省教育厅重点项目(23A0705,22A0687) （23A0705,22A0687）
湖南省教育厅教育改革研究项目(HNJG-2021-1151) （HNJG-2021-1151）
湖南省哲学社会科学基金(22JD077,19YBA142) （22JD077,19YBA142）
湖南警察学院重点研究项目(2024ZD07,2024ZD03,2024ZD04) （2024ZD07,2024ZD03,2024ZD04）
湖南警察学院教改项目 （）
湖南警察学院新型犯罪案件侦查理论与实践科研创新团队. （）
