15 citations · 25 across the 15 of their papers we have counts for
15 papers
An Efficient Ground-aerial Transportation System for Pest Control Enabled by AI-based Autonomous Nano-UAVs
Luca Crupi, Luca Butera, Alberto Ferrante +2
Efficient crop production requires early detection of pest outbreaks and timely treatments; we consider a solution based on a fleet of multiple autonomous miniaturized unmanned aer…
Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW
Elia Cereda, Alessandro Giusti, Daniele Palossi
Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Their small form fac…
Distilling Tiny and Ultra-fast Deep Neural Networks for Autonomous Navigation on Nano-UAVs
Lorenzo Lamberti, Lorenzo Bellone, Luka Macan +4
Nano-sized unmanned aerial vehicles (UAVs) are ideal candidates for flying Internet-of-Things smart sensors to collect information in narrow spaces. This requires ultra-fast naviga…
Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones
Lorenzo Lamberti, Vlad Niculescu, Michał Barcis +4
Pocket-sized autonomous nano-drones can revolutionize many robotic use cases, such as visual inspection in narrow, constrained spaces, and ensure safer human-robot interaction due…
A Deep Learning-based Pest Insect Monitoring System for Ultra-low Power Pocket-sized Drones
Luca Crupi, Luca Butera, Alberto Ferrante +1
Smart farming and precision agriculture represent game-changer technologies for efficient and sustainable agribusiness. Miniaturized palm-sized drones can act as flexible smart sen…
Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems
Luca Bompani, Manuele Rusci, Daniele Palossi +2
This paper introduces Multi-Resolution Rescored Byte-Track (MR2-ByteTrack), a novel video object detection framework for ultra-low-power embedded processors. This method reduces th…