Detection of Polymer Thickness Based on the Degree of Their Transparency in SWIR Spectrum on Edge Computing Device

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F24%3A39922173" target="_blank" >RIV/00216275:25530/24:39922173 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-96759-7_27" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-96759-7_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-96759-7_27" target="_blank" >10.1007/978-3-031-96759-7_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of Polymer Thickness Based on the Degree of Their Transparency in SWIR Spectrum on Edge Computing Device

Popis výsledku v původním jazyce

The study demonstrates that polymer (ABS) parts and their thickness can be accurately detected using SWIR camera images. We also evaluated the feasibility of performing this detection on edge computing devices like the Jetson Nano, ensuring sufficient speed for real-time applications. For this purpose, we used models from the latest versions of the YOLO object detection framework. Using a dataset of 870 images, featuring circular samples with thicknesses ranging from 0.2 to 1 mm, we trained and tested the neural networks. The YOLOv9-E model achieved the highest precision with an AP@[.5:.05:.95] of 96.57%. In particular, even smaller, faster models like the YOLOv8s demonstrated high precision (AP@[.5:.05:.95] of 96.28%) and rapid processing times, with an average inference time of 70.6 ms. These results suggest that SWIR imaging combined with YOLO models is a viable approach for real-time industrial applications, particularly in the recycling of polymer waste scenarios.

Název v anglickém jazyce

Detection of Polymer Thickness Based on the Degree of Their Transparency in SWIR Spectrum on Edge Computing Device

Popis výsledku anglicky

The study demonstrates that polymer (ABS) parts and their thickness can be accurately detected using SWIR camera images. We also evaluated the feasibility of performing this detection on edge computing devices like the Jetson Nano, ensuring sufficient speed for real-time applications. For this purpose, we used models from the latest versions of the YOLO object detection framework. Using a dataset of 870 images, featuring circular samples with thicknesses ranging from 0.2 to 1 mm, we trained and tested the neural networks. The YOLOv9-E model achieved the highest precision with an AP@[.5:.05:.95] of 96.57%. In particular, even smaller, faster models like the YOLOv8s demonstrated high precision (AP@[.5:.05:.95] of 96.28%) and rapid processing times, with an average inference time of 70.6 ms. These results suggest that SWIR imaging combined with YOLO models is a viable approach for real-time industrial applications, particularly in the recycling of polymer waste scenarios.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Spolupráce Univerzity Pardubice a aplikační sféry v aplikačně orientovaném výzkumu lokačních, detekčních a simulačních systémů pro dopravní a přepravní procesy (PosiTrans)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Artificial Intelligence and System Engineering: Proceedings of 8th Computational Methods in Systems and Software 2024, Volume 2 (Lecture Notes in Networks and Systems. Vol. 1490)

ISBN

978-3-031-96758-0

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

9

Strana od-do

1-9

Název nakladatele

Springer Science and Business Media

Místo vydání

—

Místo konání akce

online

Datum konání akce

25. 10. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—