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dc.contributor.authorDias, K.
dc.contributor.authorAnsari, S.
dc.contributor.authorAmonkar, A.
dc.contributor.editorMishra, B.K.
dc.contributor.editorBorah, S.
dc.contributor.editorKasturiwale, H.
dc.date.accessioned2022-08-31
dc.date.accessioned2023-03-01T09:15:36Z
dc.date.available2022-07-30
dc.date.available2023-03-01T09:15:36Z
dc.date.issued2022
dc.identifier.citationin: Mishra, B.K., Borah, S., Kasturiwale, H.(Eds.), Computing and Communications Engineering in Real-Time Application Development.Apple Academic Press, New York, 2022, pp.231-242
dc.identifier.isbn978-1-00-327721-7
dc.identifier.otherhttps://www.taylorfrancis.com/chapters/edit/10.1201/9781003277217-18
dc.identifier.urihttps://doi.org/10.1201/9781003277217-18
dc.identifier.urihttps://drs.nio.org/drs/handle/2264/8987
dc.description0.000
dc.description.abstractUnderwater object detection faces various challenges like turbidity, non-uniform lighting, scattering, and lack of image clarity.The application of deep learning techniques for underwater object detection has significantly improved the detection performance as compared to the traditional object detection methods.However not many attempts have been made to detect trash from underwater images mostly due to lack of images and labeled ground truth datasets.This chapter aims to evaluate the YOLACT and Faster-RCNN algorithm to perform trash detection in an underwater environment.A freely available dataset of marine debris is used to train the CNN for object detection.The trained network is then evaluated on test images to assess its fitness for real-time applications.
dc.languageen
dc.publisherApple Academic Press
dc.relation.ispartofMishra, B.K., Borah, S., Kasturiwale, H.(Eds.), Computing and Communications Engineering in Real-Time Application Development.Apple Academic Press, New York, 2022
dc.relation.isreferencedbyNON SCI
dc.rightsOnly the author version of the copright reserved papers uploaded. Contact drs@nio.org for any complaints regarding copyright violations
dc.titleMarine Trash Detection Using Deep Learning Models
dc.typeBook Chapter
dc.affiliation.authorCSIR-National Institute of Oceanography, Dona Paula, Goa, India


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