Hierarchical Object Detection applied to Fish Species

Hierarchical Object Detection of Fish Species

Authors

  • Dr. Aditya Gupta University of Agder
  • Espen Stausland Kalhagen University of Agder
  • Ørjan Langøy Olsen University of Agder
  • Dr. Morten Goodwin University of Agder https://orcid.org/0000-0001-6331-702X

DOI:

https://doi.org/10.5617/nmi.9452

Keywords:

Object Detection, Hierarchical Classification, YOLO, Fish Species

Abstract

Gathering information of aquatic life is often based on timeconsuming
methods utilizing video feeds. It would be beneficial
to 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 of
YOLO for object detection of fish. As underwater conditions
can be difficult and thus fish species are hard to discriminate.
This study proposes a hierarchical structure-based YOLO Fish
algorithm in both the classification and the dataset to gain
valuable information. With the use of hierarchical classification
and other techniques. YOLO Fish is a state-of-the-art object
detector on Nordic fish species, with an mAP of 91.8%. The
algorithm has an inference time of 26.4 ms, fast enough to
run on real-time video on the high-end GPU Tesla V100.

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Published

2022-06-03

Issue

Section

Articles
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