Addressing the challenges of varying object sizes, high target density, limited onboard hardware, and complex backgrounds in UAV-based detection, this paper presents FCCD-YOLO—a lightweight, feature-enhanced model built on the YOLOv8 framework. First, we introduce a C2f FC module in the backbone network that uses a dynamic gating mechanism to substantially reduce model parameters and FLOPs while retaining essential spatial feature extraction capabilities. Next, we construct a "sandwich" DCGF (DySample Context-Aware Guided Fusion) structure that sequentially fuses DySample, Context-Guided Fusion (CGF), and the C2f module to enhance multi-scale feature interaction and fine-detail representation. Finally, we incorporate a DS Head into the detection layer to improve boundary localization and classification accuracy for minute targets. Experimental results on the VisDrone2019 dataset show that, compared with the baseline YOLOv8s, FCCD-YOLO achieves a 1.16% increase in Precision, a 1.90% increase in Recall, a 2.37% improvement in mAP50, and a 1.68% boost in mAP50:95, while reducing model parameters and GFLOPS by 28.14% and 20.35%, respectively. These gains demonstrate FCCD-YOLO’s effectiveness and suitability for resource-constrained aerial imaging scenarios.
Key words
YOLOv8 /
Unmanned Aerial Vehicles (UAVs) /
small object detection /
DCGF "sandwich" structure /
lightweight
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