A Lightweight Feature-Fusion and Small-Target Enhancement Network for Vision-Based UAV Detection

Abstract

Detecting small unmanned aerial vehicles (UAVs) in ground-to-air imagery is challenging because their weak visual cues must be preserved without imposing excessive computation on resource-constrained platforms. To address the unresolved trade-off between tiny-target representation and deployment efficiency, we propose a Lightweight Feature-Fusion and Small-Target Enhancement Network (LFE-YOLO), a lightweight detector that coordinates partial-channel feature extraction, efficient cross-scale fusion, high-resolution prediction, background-interference suppression, and stable tiny-box regression within a unified architecture. Specifically, C2fFaster and GSConv reduce redundant computation while maintaining multi-scale feature propagation; a P2 high-resolution detection branch and Efficient Multi-scale Attention preserve fine spatial cues and suppress background interference; and Normalized Wasserstein Distance complements Complete Intersection over Union to improve tiny-box localization stability. We also construct Det-UAV by integrating newly collected multi-platform and multi-scene UAV imagery with existing data using scene- and sequence-independent partitioning and duplicate control. Experiments on Det-UAV show that LFE-YOLO improves detection accuracy while reducing parameters and computation relative to YOLOv8s. Zero-shot evaluation on the public DUT Anti-UAV dataset further indicates favorable transferability to an unseen data distribution. TensorRT 8.2.1 deployment experiments on NVIDIA Jetson TX2 show that, under the same evaluation settings, LFE-YOLO achieves higher detection accuracy and inference throughput, lower latency, and a smaller engine size than the comparable-scale YOLOv8s and YOLO11s models. These results support a practical accuracy–efficiency balance for small-UAV detection under constrained resources.