Detection of Polymer Thickness Based on the Degree of Their Transparency in SWIR Spectrum on Edge Computing Device
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Detection of Polymer Thickness Based on the Degree of Their Transparency in SWIR Spectrum on Edge Computing Device
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
<a href="/en/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
9
Pages from-to
1-9
Publisher name
Springer Science and Business Media
Place of publication
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Event location
online
Event date
Oct 25, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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