YOLO fish detection with Euclidean tracking in fish farms

dc.AffiliationOctober University for modern sciences and Arts (MSA)
dc.contributor.authorWageeh, Youssef 
dc.contributor.authorMohamed, Hussam El‑Din 
dc.contributor.authorFadl, Ali 
dc.contributor.authorAnas, Omar 
dc.contributor.authorElMasry, Noha 
dc.contributor.authorNabil, Ayman 
dc.contributor.authorAtia, Ayman 
dc.date.accessioned2021-01-06T10:06:20Z
dc.date.available2021-01-06T10:06:20Z
dc.date.issued01/03/2021
dc.description.abstractThe activities of managing fish farms, like fish ponds surveillance , are one of the tough and costly fish farmers’ missions. Generally, these activities are done manually, wasting time and money for fish farmers. A method is introduced in this paper which improves fish detection and fish trajectories where the water conditions is challenging. Image Enhancement algorithm is used at first to improve unclear images. Object Detection algorithm is then used on the enhanced images to detect fish. In the end, features like fish count and trajectories are extracted from the coordinates of the detected objects. Our method aims for better fish tracking and detection over fish ponds in fish farms.en_US
dc.description.urihttps://www.scimagojr.com/journalsearch.php?q=19400158593&tip=sid&clean=0
dc.identifier.doihttps://doi.org/10.1007/s12652-020-02847-6
dc.identifier.otherhttps://doi.org/10.1007/s12652-020-02847-6
dc.identifier.urihttps://qrgo.page.link/gf2d8
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesJournal of Ambient Intelligence and Humanized Computing;2021
dc.subjectOctober University for Fish farmingen_US
dc.subjectObject tracking en_US
dc.subjectImage enhancementen_US
dc.subjectObject detection en_US
dc.titleYOLO fish detection with Euclidean tracking in fish farmsen_US
dc.typeArticleen_US

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