Scholarlink

<p>Small-object detection in UAV remote-sensing imagery is vital to a wide range of modern applications. However, existing methods often struggle with small-scale targets, dense occlusion, and complex backgrounds, leading to missed and false detections. To address these persistent issues, this paper proposes MACE-YOLO, a multi-path aggregation and cross-scale enhanced feature fusion network based on YOLOv11. The proposed Multi-path Aggregation and Context-aware Fusion (MACF) module strengthens fine-grained feature representation in the backbone network. Additionally, the Additive Cross-scale Feature Pyramid Network (ACFPN) improves the efficiency of cross-scale information interaction through the Channel-Additive Fusion (CAF) mechanism and multi-branch cross-layer connections. The Dynamic Head (DyHead) further optimizes feature re-weighting via multi-dimensional attention, while the Dilated Shared Pyramid Convolution (DSPC) module effectively preserves the detailed features of small objects. Experimental results on the VisDrone2019 dataset show that MACE-YOLO improves <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(AR_s\)</EquationSource><EquationSource Format="MATHML"><math><mrow><mi>A</mi><msub><mi>R</mi><mi>s</mi></msub></mrow></math></EquationSource></InlineEquation>, <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(AP_s\)</EquationSource><EquationSource Format="MATHML"><math><mrow><mi>A</mi><msub><mi>P</mi><mi>s</mi></msub></mrow></math></EquationSource></InlineEquation>, and <i>mAP</i>50 over YOLOv11s by 2.3%, 2.2%, and 4.1%, respectively. It maintains a relatively low parameter count, indicating a more favorable trade-off between accuracy and efficiency. Further evaluations on the RSOD and DIOR datasets confirm the algorithm’s superior generalization ability and performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MACE-YOLO: A multi-path aggregation and cross-scale enhanced feature fusion network for small-object detection in UAV remote-sensing imagery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月21日

查看全文
  • Xingyuan Wang,
  • Jingyu Wang,
  • Jinling Yu,
  • Lixin Liu,
  • Chenyang Xue,
  • Yongxin Li,
  • Haofei Zhang

摘要

Small-object detection in UAV remote-sensing imagery is vital to a wide range of modern applications. However, existing methods often struggle with small-scale targets, dense occlusion, and complex backgrounds, leading to missed and false detections. To address these persistent issues, this paper proposes MACE-YOLO, a multi-path aggregation and cross-scale enhanced feature fusion network based on YOLOv11. The proposed Multi-path Aggregation and Context-aware Fusion (MACF) module strengthens fine-grained feature representation in the backbone network. Additionally, the Additive Cross-scale Feature Pyramid Network (ACFPN) improves the efficiency of cross-scale information interaction through the Channel-Additive Fusion (CAF) mechanism and multi-branch cross-layer connections. The Dynamic Head (DyHead) further optimizes feature re-weighting via multi-dimensional attention, while the Dilated Shared Pyramid Convolution (DSPC) module effectively preserves the detailed features of small objects. Experimental results on the VisDrone2019 dataset show that MACE-YOLO improves \(AR_s\)ARs, \(AP_s\)APs, and mAP50 over YOLOv11s by 2.3%, 2.2%, and 4.1%, respectively. It maintains a relatively low parameter count, indicating a more favorable trade-off between accuracy and efficiency. Further evaluations on the RSOD and DIOR datasets confirm the algorithm’s superior generalization ability and performance.