Detection of Marine Animals in a New Underwater Dataset with Varying Visibility

Malte Pedersen, Joakim Bruslund Haurum, Rikke Gade, Thomas B. Moeslund; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 18-26

Abstract


The increasing demand for marine monitoring calls for robust automated systems to support researchers in gathering information from marine ecosystems. This includes computer vision based marine organism detection and species classification systems. Current state-of-the-art marine vision systems are based on CNNs, which in nature require a relatively large amount of varied training data. In this paper we present a new publicly available underwater dataset with annotated image sequences of fish, crabs, and starfish captured in brackish water with varying visibility. The dataset is called the Brackish Dataset and it is the first part of a planned long term monitoring of the marine species visiting the strait where the cameras are permanently mounted. To the best of our knowledge, this is the first annotated underwater image dataset captured in temperate brackish waters. In order to obtain a baseline performance for future reference, the YOLOv2 and YOLOv3 CNNs were fine-tuned and tested on the Brackish Dataset.

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[bibtex]
@InProceedings{Pedersen_2019_CVPR_Workshops,
author = {Pedersen, Malte and Bruslund Haurum, Joakim and Gade, Rikke and Moeslund, Thomas B.},
title = {Detection of Marine Animals in a New Underwater Dataset with Varying Visibility},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}