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Title: AI-based recognition of dangerous goods labels and metric package features
Language: English
Authors: Brylka, Robert 
Bierwirth, Benjamin 
Schwanecke, Ulrich 
Editor: Kersten, Wolfgang  
Ringle, Christian M.  
Blecker, Thorsten 
Keywords: Artificial Intelligence;Blockchain
Issue Date: 1-Dec-2021
Publisher: epubli
Source: Hamburg International Conference of Logistics (HICL) 31: 245-272 (2021)
Part of Series: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Volume number: 31
Abstract (english): 
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.
Conference: Hamburg International Conference of Logistics (HICL) 2021 
DOI: 10.15480/882.3959
ISBN: 978-3-754927-70-0
ISSN: 2365-5070
Document Type: Chapter/Article (Proceedings)
Peer Reviewed: Yes
License: CC BY-SA 4.0 (Attribution-ShareAlike 4.0) CC BY-SA 4.0 (Attribution-ShareAlike 4.0)
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