AI-based recognition of dangerous goods labels and metric package features

Robert Brylka, Benjamin Bierwirth and Ulrich Schwanecke

A chapter in Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management, 2021, pp 245-272 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: 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.

Keywords: Artificial Intelligence; Blockchain (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/249618/1/hicl-2021-31-245.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:249618

DOI: 10.15480/882.3959

Access Statistics for this chapter

More chapters in Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL) from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().