Conference paper
Detection, Localization and Classification of Fish and Fish Species in Poor Conditions using Convolutional Neural Networks
In this work the initial steps towards a system capable of parametrising fish schools in underwater images are presented. For this purpose a deep convolutional neural network called Optical Fish Detection Network (OFDNet) is introduced. This is based on state-of-the-art deep learning object detection architectures and carries out the task of fish detection, localization and species classification using visual data obtained by underwater cameras.
This work is focused towards applications in the poorly conditioned North and Baltic Sea and is initially developed for the purpose of recognizing herring and mackerel. Based on experiments on a dataset obtained at sea, OFDNet is shown to successfully detect 66.7% of the fish included and furthermore classify 89.7% of these correctly.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2019 |
| Pages: | 1-6 |
| Proceedings: | 2018 IEEE OES Autonomous Underwater Vehicle Symposium |
| ISBN: | 1728102537 , 1728102545 , 9781728102535 and 9781728102542 |
| ISSN: | 23776536 and 15223167 |
| Types: | Conference paper |
| DOI: | 10.1109/AUV.2018.8729798 |
| ORCIDs: | Galeazzi, Roberto and Andersen, Jens Christian |
Artificial intelligence Convolutional neural networks Deep learning Fish detection Object detection
Computer architecture Detectors Feature extraction OFDNet Training aquaculture artificial intelligence cameras convolutional neural nets convolutional neural networks deep convolutional neural network deep learning deep learning object detection architectures fish detection fish schools fish species classification fish species detection fish species localization herring recognition image classification learning (artificial intelligence) mackerel recognition object detection object recognition optical fish detection network underwater cameras underwater images visual data
