TL;DR
The proposed AeroFineFusion-Det introduces a fine-scale bidirectional multi-scale fusion strategy to improve information flow across feature levels, together with a dual-attention weighted fusion mechanism that adaptively enhances discriminative representations.
HD-YOLO improves small-object detection with a compact parameter footprint, while direct hardware benchmarks remain necessary to establish deployment efficiency.
Experimental results on public benchmark datasets demonstrate that the proposed SMDS-Net achieves highly competitive detection accuracy while significantly reducing the number of model parameters and theoretical computational complexity.
A lightweight multi-scale enhanced detection framework tailored for complex UAV scenarios, built upon a MobileNet backbone, that introduces a multi-scale enhancement module that constructs a feature pyramid and incorporates a scale-adaptive fusion mechanism to dynamically reweight the contributions of features from dif...
SSM-YOLO11s is proposed, a lightweight model optimized for small object detection in aerial imagery that achieves a superior balance between precision and efficiency compared to state-of-the-art models.
Small vehicle detection in uncrewed aerial vehicle (UAV) imagery is severely hindered by low target resolution, drastic scale variations, and complex background occlusion, yielding high false-negative rates in conventional detectors. Here, we propose an enhanced YOLO11 architecture optimized to resolve these limitation...
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 is propose...
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