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AI-based recognition of dangerous goods labels and metric package features
Citation Link: https://doi.org/10.15480/882.3959
Publikationstyp
Conference Paper
Date Issued
2021-12-01
Sprache
English
Herausgeber*innen
TORE-DOI
First published in
Number in series
31
Start Page
245
End Page
272
Citation
Hamburg International Conference of Logistics (HICL) 31: 245-272 (2021)
Contribution to Conference
Publisher
epubli
Peer Reviewed
true
Purpose: Dangerous goods shipments require special labeling, which has to be checked manually every time a shipment is handed over in the supply chain. We describe an AI-based detection methodology to automate the recognition of dangerous goods labels and other shipment features (such as single piece volume detection).
Methodology: We use five industry RGB cameras and three AZURE RGBD cameras to generate images from shipments passing through a gate. The images are processed based on the YOLO detector to identify and separate dangerous goods labels and barcodes. We trained YOLO for our particular problem with about 1.000 manually labeled and 50.000 artificial generated images.
Findings: While dangerous goods labels detection was successfully validated in a laboratory environment and a warehouse, volume detection for single pieces consolidated on a pallet could be conceptualized. The system shows a high detection rate combined with fast processing, where the addition of computer-generated training images significantly improves the recognition rate for complex backgrounds.
Originality: Parallel detection of multiple package features (volume, barcode, dangerous goods labels) of multiple pieces consolidated on a pallet is not available yet. Our solution processes a shipment faster and more accurately than existing single-piece solutions without restrictions to the material flow.
Methodology: We use five industry RGB cameras and three AZURE RGBD cameras to generate images from shipments passing through a gate. The images are processed based on the YOLO detector to identify and separate dangerous goods labels and barcodes. We trained YOLO for our particular problem with about 1.000 manually labeled and 50.000 artificial generated images.
Findings: While dangerous goods labels detection was successfully validated in a laboratory environment and a warehouse, volume detection for single pieces consolidated on a pallet could be conceptualized. The system shows a high detection rate combined with fast processing, where the addition of computer-generated training images significantly improves the recognition rate for complex backgrounds.
Originality: Parallel detection of multiple package features (volume, barcode, dangerous goods labels) of multiple pieces consolidated on a pallet is not available yet. Our solution processes a shipment faster and more accurately than existing single-piece solutions without restrictions to the material flow.
Subjects
Artificial Intelligence
Blockchain
DDC Class
330: Wirtschaft
380: Handel, Kommunikation, Verkehr
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