Efficient Real-Time Quadcopter Propeller Detection and Attribute Estimation with High-Resolution Event Camera

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00386495" target="_blank" >RIV/68407700:21230/25:00386495 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-95911-0_16" target="_blank" >https://doi.org/10.1007/978-3-031-95911-0_16</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-95911-0_16" target="_blank" >10.1007/978-3-031-95911-0_16</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient Real-Time Quadcopter Propeller Detection and Attribute Estimation with High-Resolution Event Camera

Popis výsledku v původním jazyce

In this paper, we present a computationally efficient method for real-time detection and state estimation of quadcopter propellers in high-resolution event-camera streams. We model local event arrivals as Poisson processes and exploit the memoryless nature of inter-arrival times to robustly detect periodic bursts from rotating blades, even at high rotational speeds. Unlike approaches that process data in chunks, our method updates the detection metrics for each incoming event. Once a propeller is detected, we first calculate its angular speed and then fit an ellipse to the aggregated propeller events to estimate pitch and roll. We introduce a new dataset (speeds 1100–8200 RPM; tilt angles 0∘, 10∘, and 90∘) and achieve near-perfect detection accuracy at an average real-time factor of 0.94 on a single CPU core, demonstrating the suitability of the approach for onboard deployment.

Název v anglickém jazyce

Efficient Real-Time Quadcopter Propeller Detection and Attribute Estimation with High-Resolution Event Camera

Popis výsledku anglicky

In this paper, we present a computationally efficient method for real-time detection and state estimation of quadcopter propellers in high-resolution event-camera streams. We model local event arrivals as Poisson processes and exploit the memoryless nature of inter-arrival times to robustly detect periodic bursts from rotating blades, even at high rotational speeds. Unlike approaches that process data in chunks, our method updates the detection metrics for each incoming event. Once a propeller is detected, we first calculate its angular speed and then fit an ellipse to the aggregated propeller events to estimate pitch and roll. We introduce a new dataset (speeds 1100–8200 RPM; tilt angles 0∘, 10∘, and 90∘) and achieve near-perfect detection accuracy at an average real-time factor of 0.94 on a single CPU core, demonstrating the suitability of the approach for onboard deployment.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Image Analysis

ISBN

978-3-031-95911-0

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

14

Strana od-do

217-230

Název nakladatele

Springer, Cham

Místo vydání

—

Místo konání akce

Reykjavik

Datum konání akce

23. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001553875500016