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YOLOFLY: A Consumer-Centric Framework for Efficient Object Detection in UAV Imagery
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AI
DOI：10.3390/electronics14030498.png)
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Abstract
En 中文
As an emerging edge device aimed at consumers, Unmanned Aerial Vehicles (UAVs) have attracted significant attention in the consumer electronics market, particularly for intelligent imaging applications. However, aerial image detection tasks face two major challenges: first, there are numerous small and overlapping objects that are difficult to identify from an aerial perspective, and second, if the detection frame rate is not high enough, missed detections may occur when the UAV is moving quickly, which can negatively impact the user experience by reducing detection accuracy, increasing the likelihood of collision-avoidance failures, and potentially causing unsafe flight behavior. To address these challenges, this paper proposes a novel YOLO (you only look once) framework, named YOLOFLY, which includes a C4f feature extraction module and a DWcDetect head to make the model lightweight, as well as an MPSA attention mechanism and an ACIoU loss function, aimed at improving detection accuracy and performance for consumer-grade UAVs. Extensive experiments on the public VisDrone2019 dataset demonstrate that YOLOFLY outperforms the latest state-of-the-art model, YOLOv11n, by 3.2% in mAP50-95, reduces detection time by 27.2 ms, decreases the number of parameters by 0.6 M, and cuts floating-point operations by 1.8 B. Finally, testing YOLOFLY in real-world environments also yielded the best results, including a 3.75% reduction in missed detections at high speeds. These findings validate the superiority and effectiveness of YOLOFLY.
Keywords:
UAV
consumer-centric
object detection
YOLO
C4f
DWcDetect
MPSA
ACIoU
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