Hierarchical Object Detection applied to Fish Species Hierarchical Object Detection of Fish Species

Gathering information of aquatic life is often based on timeconsumingmethods utilizing video feeds. It would be beneficialto capture more information cost-effectively from video feeds.Video based object detection has an ability to achieve this.Recent research has shown promising results with the use...

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Bibliographic Details
Published inNordic Machine Intelligence Vol. 2; no. 1
Main Authors Gupta, Dr. Aditya, Kalhagen, Espen Stausland, Olsen, Ørjan Langøy, Goodwin, Morten
Format Journal Article
LanguageEnglish
Published 03.06.2022
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Summary:Gathering information of aquatic life is often based on timeconsumingmethods utilizing video feeds. It would be beneficialto capture more information cost-effectively from video feeds.Video based object detection has an ability to achieve this.Recent research has shown promising results with the use ofYOLO for object detection of fish. As underwater conditionscan be difficult and thus fish species are hard to discriminate.This study proposes a hierarchical structure-based YOLO Fishalgorithm in both the classification and the dataset to gainvaluable information. With the use of hierarchical classificationand other techniques. YOLO Fish is a state-of-the-art objectdetector on Nordic fish species, with an mAP of 91.8%. Thealgorithm has an inference time of 26.4 ms, fast enough torun on real-time video on the high-end GPU Tesla V100.
ISSN:2703-9196
2703-9196
DOI:10.5617/nmi.9452