Abstract
The YOLO series of object detection methods have achieved significant success in a wide range of computer vision tasks due to their efficiency and accuracy. However, detecting small objects in UAV images remains a formidable challenge due to factors such as a low resolution, complex background interference, and significant scale variations, which collectively degrade the quality of feature extraction and limit detection performance. To address these challenges, we propose the receptive field attention-guided YOLO (RFAG-YOLO) method, an advanced adaptation of YOLOv8 tailored for small-object detection in UAV imagery, with a focus on improving feature representation and detection robustness. To this end, we introduce a novel network building block, termed the receptive field network block (RFN block), which leverages dynamic kernel parameter adjustments to enhance the model's ability to capture fine-grained local details. To effectively harness multi-scale features, we designed an enhanced FasterNet module based on RFN blocks as the core component of the backbone network in RFAG-YOLO, enabling robust feature extraction across varying resolutions. This approach achieves a balance of semantic information by employing staged downsampling and a hierarchical arrangement of RFN blocks, ensuring optimal feature representation across different resolutions. Additionally, we introduced a Scale-Aware Feature Amalgamation (SAF) component prior to the detection head of RFAG-YOLO. This component employs a scale attention mechanism to dynamically weight features from both higher and lower layers, facilitating richer information flow and significantly improving the model's robustness to complex backgrounds and scale variations. Experimental results on the VisDrone2019 dataset demonstrated that RFAG-YOLO outperformed state-of-the-art models, including YOLOv7, YOLOv8, YOLOv10, and YOLOv11, in terms of detection accuracy and efficiency. In particular, RFAG-YOLO achieved an mAP50 of 38.9%, representing substantial improvements over multiple baseline models: a 12.43% increase over YOLOv7, a 5.99% improvement over YOLOv10, and significant gains of 16.12% compared to YOLOv8n and YOLOv11. Moreover, compared to the larger YOLOv8s model, RFAG-YOLO achieved 97.98% of its mAP50 performance while utilizing only 53.51% of the parameters, highlighting its exceptional efficiency in terms of the performance-to-parameter ratio and making it highly suitable for resource-constrained UAV applications. These results underscore the substantial potential of RFAG-YOLO for real-world UAV applications, particularly in scenarios demanding accurate detection of small objects under challenging conditions such as varying lighting, complex backgrounds, and diverse scales.