Printed circuit board identification using deep convolutional neural networks to facilitate recycling

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dc.contributor.authorSoomro, Iftikhar A.
dc.contributor.authorAhmad, Anser
dc.contributor.authorRaza, Rana H.
dc.date.accessioned2021-11-08T13:54:19Z
dc.date.available2021-11-08T13:54:19Z
dc.date.issued2021-10-25
dc.description.abstractIn this paper, we have proposed a robust Printed Circuit Board (PCB) classification system based on computer vision and deep learning to assist sorting e-waste for recycling. We have used a public PCB dataset acquired using a conveyor belt, as well as a locally developed PCB dataset that represents challenging practical conditions such as varying lighting, orientation, distance from camera, cast shadows, view-points and different cameras/resolutions. A pre-trained EfficientNet-B3 deep learning model is utilized and retrained for use with our data in PCB classification context. Deep nets are designed for closed set recognition tasks capable of classifying only the images they have been trained for. We have extended the closed set nature of deep nets for use in our open set classification tasks which require identifying unknown PCBs apart from classifying known PCBs. We have achieved an open set average accuracy of 92.4% which is state of the art given the complexities in the datasets we worked with.en_UK
dc.identifier.citationSoomro IA, Ahmad A, Raza RH. (2022) Printed circuit board identification using deep convolutional neural networks to facilitate recycling, Resources. Conservation and Recycling, Volume 177, February 2022, Article number 105963en_UK
dc.identifier.issn0921-3449
dc.identifier.urihttps://doi.org/10.1016/j.resconrec.2021.105963
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17251
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDeep convolutional neural networksen_UK
dc.subjectPrinted circuit boarden_UK
dc.subjectPrinted electronic circuiten_UK
dc.subjectPCB identificationen_UK
dc.subjectClassificationen_UK
dc.subjectRecyclingen_UK
dc.titlePrinted circuit board identification using deep convolutional neural networks to facilitate recyclingen_UK
dc.typeArticleen_UK

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