计算机科学与探索2026，Vol.20Issue(1)：266-279,14.DOI:10.3778/j.issn.1673-9418.2506003
DEPA-YOLO:无人机视角下的小目标检测模型
DEPA-YOLO:Drone-Based Small Object Detection Model
摘要
Abstract
In response to the challenges of small object detection from the UAV perspective,including dense target distri-bution,complex background interference,and insufficient feature resolution,this paper proposes an improved model based on YOLOv10,named DEPA-YOLO.A DCMB(dynamic cross-modal bottleneck)is designed,which integrates dynamic weight allocation and multi-pattern feature mixing strategies to enhance the joint modeling capability of shallow local textures and high-level global semantics,thereby improving the feature representation of small objects.A HFEP(hierarchical feature enhancement pyramid)structure is proposed,which combines SPDConv(space-to-depth convolution)with ECFI(efficient cross-branch feature integration)multi-branch dynamic fusion to preserve fine details across different scales and efficiently transmit multi-level semantic information,significantly alleviating the information loss of traditional feature pyramids in dense scenarios.A CPCA(channel prior convolutional attention)attention mechanism is embedded into the backbone network to guide the model to focus on salient target regions,reducing false positives and missed detections caused by complex backgrounds.A WIoU(wise intersection over union)regression loss is adopted,introducing gradient regulation and boundary penalty terms to improve the stability and robustness of small object localization.Ablation experi-ments on the VisDrone2019 dataset demonstrate that DEPA-YOLO improves precision,mAP50,and mAP50:95 by 4.7,4.3,and 2.5 percentage points,respectively,compared with the YOLOv10 baseline.Furthermore,generalization experi-ments on the DOTA dataset show additional improvements of 7.9,2.0,and 1.2 percentage points.The proposed DEPA-YOLO model significantly enhances the detection accuracy of small objects from UAV perspectives while maintaining efficiency,providing an effective solution for UAV-based small object detection.关键词
小目标检测/YOLOv10/特征增强金字塔/注意力机制Key words
small object detection/YOLOv10/feature enhancement pyramid/attention mechanism分类
信息技术与安全科学引用本文复制引用
刘臣杰,刘巍,杨雯迪,王成..DEPA-YOLO:无人机视角下的小目标检测模型[J].计算机科学与探索,2026,20(1):266-279,14.基金项目
国家自然科学基金(62073125).This work was supported by the National Natural Science Foundation of China(62073125). （62073125）
