AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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...
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.
HD-YOLO improves small-object detection with a compact parameter footprint, while direct hardware benchmarks remain necessary to establish deployment efficiency.
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.
After dataset-specific training and evaluation on the markedly different TinyPerson benchmark, HSF-Net outperforms YOLOv11s by 5.2 percentage points in mAP50, indicating that its relative performance advantage persists under a substantially different target-scale distribution and maritime background.
This work proposes RAD-YOLO, a YOLOv8s-based small-object detector, and develops an edge-deployable variant named RAD-YOLO-Slim, which provides a practical balance of accuracy, speed and energy efficiency on the RK3576 platform.
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