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This study aims to enhance object detection performance for small targets in UAV imagery using a lightweight network architecture.

May 9, 2026SensorsOpen Access

RFE-YOLO: A Lightweight Receptive Field-Enhanced Network for UAV Imagery Object Detection

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Randomized trial demonstrates high-precision object detection in UAV imagery, suggesting improved performance for small object localization.

This study aims to enhance object detection performance for small targets in UAV imagery using a lightweight network architecture.
Developed RFE-YOLO with Cross-Scale Receptive Field Enhancement for improved spatial detail preservation.
Introduced dynamic inception blocks to adaptively capture features of slender objects.
Incorporated a Multi-feature Fusion Module for enhanced feature integration.
Achieved state-of-the-art mAP50 of 42.70%, a 9.3% increase from baseline YOLO11n.
Model retains only 1.91 million parameters, ensuring efficiency for real-time UAV applications.

Peng et al. (2026) studied this question.

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Randomized trial demonstrates high-precision object detection in UAV imagery, suggesting improved performance for small object localization.