Scholarlink

<p>Unmanned Aerial Vehicle (UAV) image object detection plays a crucial role in various fields. However, compared with natural images, UAV images are characterized by significant target scale variations, complex backgrounds, dense small targets, and clustered target distributions, which pose serious challenges to object detection tasks. To address these issues, this study proposes an improved object detection algorithm named MFRA-YOLO, which combines multi-scale feature fusion and receptive-field attention-based convolution, built upon the baseline YOLOv8n algorithm. First, Monte Carlo attention is integrated into receptive-field attention-based convolution to enhance cross-scale information interaction capability, thereby forming a novel convolutional module for downsampling operations. Second, a multi-scale selective fusion module is incorporated into the feature fusion network to enable adaptive cross-scale integration of features. When coupled with the scale sequence feature fusion module, this integration significantly enhances the detection performance for small targets. Finally, the Focaler-PIoUv2 loss function is designed to replace the CIoU in the baseline algorithm. This replacement allows the algorithm to better balance hard and easy samples and improve detection accuracy. Experimental results on the public dataset VisDrone2019 show that MFRA-YOLO achieves superior accuracy-efficiency tradeoffs compared to the baseline YOLOv8n and other YOLO variants. Compared to YOLOv8n, MFRA-YOLO improves mAP50 by 3.5% and mAP50:95 by 2.3%, respectively. These performance gains are accompanied by only modest increases in parameter count and computational cost. Notably, while maintaining performance at 143 FPS, thereby satisfying the real-time deployment requirements for UAVs. Furthermore, compared with several state-of-the-art algorithms designed for UAV scenarios, MFRA-YOLO also offers distinct advantages. To verify the generalization ability and stability of MFRA-YOLO, comparative experiments are carried out on the RSOD dataset, where our algorithm consistently demonstrates excellent detection performance. Overall, these results confirm that MFRA-YOLO not only improves the detection performance for UAV imagery substantially but also achieves an excellent balance between detection accuracy and efficiency.</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

An improved UAV image object detection algorithm combining multi-scale feature fusion and receptive-field attention-based convolution

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月24日

查看全文
  • Fang Dong,
  • Binbin Gui,
  • Wenfeng Wang,
  • Wenjie Fan,
  • Qihang Liu

摘要

Unmanned Aerial Vehicle (UAV) image object detection plays a crucial role in various fields. However, compared with natural images, UAV images are characterized by significant target scale variations, complex backgrounds, dense small targets, and clustered target distributions, which pose serious challenges to object detection tasks. To address these issues, this study proposes an improved object detection algorithm named MFRA-YOLO, which combines multi-scale feature fusion and receptive-field attention-based convolution, built upon the baseline YOLOv8n algorithm. First, Monte Carlo attention is integrated into receptive-field attention-based convolution to enhance cross-scale information interaction capability, thereby forming a novel convolutional module for downsampling operations. Second, a multi-scale selective fusion module is incorporated into the feature fusion network to enable adaptive cross-scale integration of features. When coupled with the scale sequence feature fusion module, this integration significantly enhances the detection performance for small targets. Finally, the Focaler-PIoUv2 loss function is designed to replace the CIoU in the baseline algorithm. This replacement allows the algorithm to better balance hard and easy samples and improve detection accuracy. Experimental results on the public dataset VisDrone2019 show that MFRA-YOLO achieves superior accuracy-efficiency tradeoffs compared to the baseline YOLOv8n and other YOLO variants. Compared to YOLOv8n, MFRA-YOLO improves mAP50 by 3.5% and mAP50:95 by 2.3%, respectively. These performance gains are accompanied by only modest increases in parameter count and computational cost. Notably, while maintaining performance at 143 FPS, thereby satisfying the real-time deployment requirements for UAVs. Furthermore, compared with several state-of-the-art algorithms designed for UAV scenarios, MFRA-YOLO also offers distinct advantages. To verify the generalization ability and stability of MFRA-YOLO, comparative experiments are carried out on the RSOD dataset, where our algorithm consistently demonstrates excellent detection performance. Overall, these results confirm that MFRA-YOLO not only improves the detection performance for UAV imagery substantially but also achieves an excellent balance between detection accuracy and efficiency.