航空兵器2025，Vol.32Issue(2)：94-103,10.DOI:10.12132/ISSN.1673-5048.2024.0064
基于YOLO-DSBE的无人机对地目标检测
UAV-to-Ground Target Detection Based on YOLO-DSBE
摘要
Abstract
To address the issues of complex background,small target scale,mutual occlusion and high missed de-tection rate in UAV captured images,this paper proposes a ground target detection algorithm based on YOLO-DSBE.The DC-ELAN and DC-MP modules based on deformable convolution are proposed to adapt to input features of different shapes and sizes,and to improve the network's ability to parse features in complex backgrounds;A high-resolution multi-scale detection layer is designed to boost the algorithm's capability in extracting small target features,thereby im-proving the detection accuracy of minute targets.The algorithm deeply integrates the BiFormer dynamic sparse attention mechanism into the improved feature fusion network,eliminating irrelevant feature information,enhancing the focus on pertinent details,and reducing the missed detection rate.Moreover,the EIoU boundary loss function is incorporated to address the ineffectiveness of the CIoU shape penalty term,enhancing model convergence speed and detection accura-cy.The experimental results show that the improved algorithm achieves an average accuracy of 56.1％on the UA-DET-RAC dataset,and compared to the original algorithms,and improve by 3.5％and 2.8％respectively on the Vis-Drone2019 dataset,effectively improving the accuracy of UAV image recognition.关键词
目标检测/无人机图像/YOLO-DSBE/可变形卷积/BiFormerKey words
object detection/UAV image/YOLO-DSBE/deformable ConvNets/BiFormer分类
武器工业引用本文复制引用
孟鹏帅,王峰,翟伟光,马星宇,赵薇..基于YOLO-DSBE的无人机对地目标检测[J].航空兵器,2025,32(2):94-103,10.基金项目
山西省留学人员科技活动项目择优资助项目(20230063) （20230063）
山西省重点研发计划(202102150101008) （202102150101008）
