TL;DR
An object detection model, FUS-DETR, specifically designed for UAV target detection in air-to-air scenarios, using a transformer-based architecture and incorporating three key innovations, demonstrating the strong potential of this model for air-to-air UAV detection tasks.
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...
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.
Experiments show that MDF-YOLO provides a favorable balance between detection accuracy and model complexity and exhibits potential for practical UAV-based object detection applications, and category-wise evaluation further demonstrates consistent AP@50 improvements across all 10 object categories.
CAF-YOLO, a Complementary Alignment and Fusion Object Detection Model based on YOLO11 improves UAV-based visual measurement reliability through three designs, and is effective in another aerial-view vehicle-detection scenario, supporting UAV-based visual measurement applications.
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...
HD-YOLO improves small-object detection with a compact parameter footprint, while direct hardware benchmarks remain necessary to establish deployment efficiency.
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