SENSOR FUSION ACROSS A WIDE RANGE OF THE ELECTROMAGNETIC SPECTRUM FOR PLASTICS IDENTIFICATION

Methods and systems for using multiple hyperspectral cameras sensitive to different wavelengths to predict characteristics of objects for further processing, including recycling, are described. The multiple hyperspectral images can be used to predict higher resolution spectra by using a trained mach...

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Bibliographische Detailangaben
Hauptverfasser:	HOLIDAY, Alexander, BADHWAR, Shruti, BANATAO, Diosdado, MURPHY, Gearoid, ZHAO, Allen Richard
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	CALCULATING COLORIMETRY COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRICITY INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT,POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED,VISIBLE OR ULTRA-VIOLET LIGHT MEASURING PERFORMING OPERATIONS PHYSICS PICTORIAL COMMUNICATION, e.g. TELEVISION POSTAL SORTING RADIATION PYROMETRY SEPARATING SOLIDS FROM SOLIDS SORTING SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTEDPIECE-MEAL, e.g. BY PICKING TESTING TRANSPORTING
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Beschreibung
Zusammenfassung:	Methods and systems for using multiple hyperspectral cameras sensitive to different wavelengths to predict characteristics of objects for further processing, including recycling, are described. The multiple hyperspectral images can be used to predict higher resolution spectra by using a trained machine learning model. The higher resolution spectra may be more easily analyzed to sort plastics into a recyclability category. The hyperspectral images may also be used to identify and analyze dark or black plastics, which are challenging for SWIR, MWIR, and other wavelengths. The machine learning model may also predict the base polymers and contaminants of plastic objects for recycling. The hyperspectral images may be used to predict recyclability and other characteristics using a trained machine learning model.