RFAG-YOLO: A Receptive Field Attention-Guided YOLO Network for Small-Object Detection in UAV Images

Chengmeng Wei ¹, Wenhong Wang ¹

⁰College of Computer Science, Liaocheng University, Liaocheng 252059, China.

Sensors (basel, Switzerland) +

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April 12, 2025

View abstract on PubMed

Summary

Receptive Field Attention-Guided YOLO (RFAG-YOLO) enhances small object detection in UAV images by improving feature extraction and robustness. This method achieves superior accuracy and efficiency compared to existing YOLO models, making it ideal for real-world applications.

Area Of Science

Computer Vision
Object Detection
UAV Imagery Analysis

Background

YOLO object detection methods are efficient but struggle with small objects in UAV images due to low resolution and scale variations.
Challenges include degraded feature extraction and limited detection performance in complex environments.

Purpose Of The Study

To develop an advanced YOLO adaptation for robust small-object detection in UAV imagery.
To improve feature representation and detection accuracy under challenging conditions.

Main Methods

Proposed Receptive Field Attention-Guided YOLO (RFAG-YOLO), an adaptation of YOLOv8.
Introduced a novel Receptive Field Network (RFN) block for fine-grained detail capture.
Designed an enhanced FasterNet module and a Scale-Aware Feature Amalgamation (SAF) component.

Main Results

RFAG-YOLO outperformed YOLOv7, YOLOv8, YOLOv10, and YOLOv11 on the VisDrone2019 dataset.
Achieved an mAP50 of 38.9%, with significant improvements over baseline models.
Demonstrated high efficiency, achieving 97.98% of YOLOv8s performance with only 53.51% of its parameters.

Conclusions

RFAG-YOLO offers superior accuracy and efficiency for small-object detection in UAVs.
The method is highly suitable for resource-constrained UAV applications.
Shows significant potential for real-world applications requiring precise detection under challenging conditions.

Keywords:

UAV images YOLOv8 attention mechanism feature extraction small-object detection