AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work provides a data-driven framework for configuring detectors on UAVs, enabling practitioners to maximize performance for specific mission profiles and enhancing the reliability of autonomous search systems.
Unmanned Aerial Vehicle (UAV) imagery presents unique challenges for object detection due to extreme variations in object scale, high density, and complex backgrounds. However, existing literature predominantly addresses these challenges by proposing computationally heavy architectural modifications, which are often im...
The results demonstrate the capability of the proposed UAV platform to perform onboard human detection and GPS-based localization in real-time, providing a basis for UAV-assisted search and rescue applications.
Real-time object detection is a core capability for autonomous robots, unmanned aerial vehicles (UAVs), and self-driving systems operating in resource-constrained environments. This paper presents spatial-channel enhanced multiscale attention (SCEMA), a novel lightweight attention module designed to enhance robotic per...
Unmanned aerial vehicles (UAVs) searching for ground targets from a high altitude face a unique challenge, particularly when the target is already within the field of view but effectively unobservable because of its small apparent scale. Standard object detectors often underperform in such scenarios because of resoluti...
Low-altitude remote sensing utilizing unmanned aerial vehicles (UAVs) has evolved into a core pillar of the space–air–ground integrated perception architecture. However, research on detection tasks in UAV aerial imagery grapples with three principal hurdles: feature annihilation of small targets, insufficient robustnes...
A deep neural network framework designed specifically to address this gap is presented, detailing its validation from a laboratory proof-of-concept to at-sea trials that validate that the DNN can reliably estimate a UAS’s bearing relative to a naval vessel.
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