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Klos ME, Pagani P (2021). Using Deep Neural Networks to Measure Puffer Levels in Real-time with Edge-Computing. Logistics Journal : Proceedings, Vol. 2021. (urn:nbn:de:0009-14-54221)

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%0 Journal Article
%T Using Deep Neural Networks to Measure Puffer Levels in Real-time with Edge-Computing
%A Klos, Matthias Elia
%A Pagani, Paolo
%J Logistics Journal : Proceedings
%D 2021
%V 2021
%N 17
%@ 2192-9084
%F klos2021
%X Technological advances and increasing data traffic in the IoT environment lead to the relocation of sophisticated data processing to the edge of networks. At the same time, powerful object detection approaches based on deep neural networks have been developed in recent years. In this paper, an intelligent camera based on deep learning algorithms and consisting of low-cost hardware with limited computational and storage capacity is presented. The developed object detection solution enables real-time monitoring of the inventory of filled and empty small load carriers in a buffer zone.
%L 620
%K Computer Vision
%K Deep Learning
%K Einplatinencomputer
%K Object Detection
%K Objekterkennung
%K Single-Board Computer
%K YOLO
%R 10.2195/lj_Proc_klos_en_202112_01
%U http://nbn-resolving.de/urn:nbn:de:0009-14-54221
%U http://dx.doi.org/10.2195/lj_Proc_klos_en_202112_01
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@Article{klos2021,
  author = 	"Klos, Matthias Elia
		and Pagani, Paolo",
  title = 	"Using Deep Neural Networks to Measure Puffer Levels in Real-time with Edge-Computing",
  journal = 	"Logistics Journal : Proceedings",
  year = 	"2021",
  volume = 	"2021",
  number = 	"17",
  keywords = 	"Computer Vision; Deep Learning; Einplatinencomputer; Object Detection; Objekterkennung; Single-Board Computer; YOLO",
  abstract = 	"Technological advances and increasing data traffic in the IoT environment lead to the relocation of sophisticated data processing to the edge of networks. At the same time, powerful object detection approaches based on deep neural networks have been developed in recent years. In this paper, an intelligent camera based on deep learning algorithms and consisting of low-cost hardware with limited computational and storage capacity is presented. The developed object detection solution enables real-time monitoring of the inventory of filled and empty small load carriers in a buffer zone.",
  issn = 	"2192-9084",
  doi = 	"10.2195/lj_Proc_klos_en_202112_01",
  url = 	"http://nbn-resolving.de/urn:nbn:de:0009-14-54221"
}
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RIS

TY  - JOUR
AU  - Klos, Matthias Elia
AU  - Pagani, Paolo
PY  - 2021
DA  - 2021//
TI  - Using Deep Neural Networks to Measure Puffer Levels in Real-time with Edge-Computing
JO  - Logistics Journal : Proceedings
VL  - 2021
IS  - 17
KW  - Computer Vision
KW  - Deep Learning
KW  - Einplatinencomputer
KW  - Object Detection
KW  - Objekterkennung
KW  - Single-Board Computer
KW  - YOLO
AB  - Technological advances and increasing data traffic in the IoT environment lead to the relocation of sophisticated data processing to the edge of networks. At the same time, powerful object detection approaches based on deep neural networks have been developed in recent years. In this paper, an intelligent camera based on deep learning algorithms and consisting of low-cost hardware with limited computational and storage capacity is presented. The developed object detection solution enables real-time monitoring of the inventory of filled and empty small load carriers in a buffer zone.
SN  - 2192-9084
UR  - http://nbn-resolving.de/urn:nbn:de:0009-14-54221
DO  - 10.2195/lj_Proc_klos_en_202112_01
ID  - klos2021
ER  -
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Wordbib

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ISI

PT Journal
AU Klos, M
   Pagani, P
TI Using Deep Neural Networks to Measure Puffer Levels in Real-time with Edge-Computing
SO Logistics Journal : Proceedings
PY 2021
VL 2021
IS 17
DI 10.2195/lj_Proc_klos_en_202112_01
DE Computer Vision; Deep Learning; Einplatinencomputer; Object Detection; Objekterkennung; Single-Board Computer; YOLO
AB Technological advances and increasing data traffic in the IoT environment lead to the relocation of sophisticated data processing to the edge of networks. At the same time, powerful object detection approaches based on deep neural networks have been developed in recent years. In this paper, an intelligent camera based on deep learning algorithms and consisting of low-cost hardware with limited computational and storage capacity is presented. The developed object detection solution enables real-time monitoring of the inventory of filled and empty small load carriers in a buffer zone.
ER
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Mods

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    <title>Using Deep Neural Networks to Measure Puffer Levels in Real-time with Edge-Computing</title>
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  <subject>
    <topic>Computer Vision</topic>
    <topic>Deep Learning</topic>
    <topic>Einplatinencomputer</topic>
    <topic>Object Detection</topic>
    <topic>Objekterkennung</topic>
    <topic>Single-Board Computer</topic>
    <topic>YOLO</topic>
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Full Metadata

Bibliographic Citation Logistics Journal : referierte Veröffentlichungen, Vol. 2021, Iss. 17
Title

Using Deep Neural Networks to Measure Puffer Levels in Real-time with Edge-Computing (eng)
Author Matthias Elia Klos, Paolo Pagani
Language eng
Abstract Technological advances and increasing data traffic in the IoT environment lead to the relocation of sophisticated data processing to the edge of networks. At the same time, powerful object detection approaches based on deep neural networks have been developed in recent years. In this paper, an intelligent camera based on deep learning algorithms and consisting of low-cost hardware with limited computational and storage capacity is presented. The developed object detection solution enables real-time monitoring of the inventory of filled and empty small load carriers in a buffer zone. Der technologische Fortschritt und zunehmende Datenströme im IoT-Umfeld führen dazu, dass anspruchsvolle Datenverarbeitungsprozesse an den Rand von Netzwerken verlagert werden. Gleichzeitig wurden in den letzten Jahren leistungsfähige Objekterkennungsansätze entwickelt, die auf tiefen neuronalen Netzen basieren. Im Rahmen dieser Arbeit wird eine intelligente Kamera vorgestellt, welche auf Deep-Learning-Algorithmen basiert und aus kostengünstiger Hardware mit beschränkter Rechen- und Speicherkapazität besteht. Die entwickelte Objekterkennungslösung ermöglicht die Überwachung des Bestands von gefüllten und leeren Kleinladungsträgern in einer Pufferzone in Echtzeit.
Subject Computer Vision, Deep Learning, Einplatinencomputer, Object Detection, Objekterkennung, Single-Board Computer, YOLO
DDC 620
Rights fDPPL
URN: urn:nbn:de:0009-14-54221
DOI https://doi.org/10.2195/lj_Proc_klos_en_202112_01