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Item type:Publication, Access status: with files , AI-based recognition of dangerous goods labels and metric package featuresPurpose: 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.Publicationtype: Conference Paper279 405 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , SmartAirCargoTrailer : autonomous short distance transports in air cargo(epubli, 2019-09-26); ; ; ; Purpose: SmartAirCargoTrailers improve short-distance air cargo transports within airport premises. They create real-time-transparency between the partners in the transport chain and thereby reduces waiting times while at the same time increasing equipment utilization. An autonomous truck with swap bodies facilitates these short-distance transports. Methodology: The SmartAirCargoTrailer system consist of an autonomous truck with swap trailers which is controlled by a cloud platform. To allow for real-timetransparency all shipments loaded onto the trailer are detected by a camera-based system. Findings: To enable reliable scannings of AirwayBill numbers (AWBs) a system of multiple cameras was developed. Challenges arose from the big variationin shipment size and scanning in motion. While the truck could be triggered automatically based on time or filling level, also human interaction had to be integrated. Originality: The system introduces a combined push-pull-algorithm to optimize the utilization of the autonomous trucks. The camera-based barcode scanning allows for shipment identification without interrupting the loading process. Autonomous driving in the mixed traffic environments of the landside airport premises is another innovative part of the project.Publicationtype: Conference Paper478 416