Resumé

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.
OriginalsprogEngelsk
TitelIEEE Conference on Computer Vision and Pattern Recognition Workshops
Publikationsdatojun. 2019
StatusAccepteret/In press - jun. 2019

Citer dette

@inproceedings{635c6c149a26430499ffc610fb565f4b,
title = "Detection of Marine Animals in a New Underwater Dataset with Varying Visibility",
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.",
author = "Malte Pedersen and Haurum, {Joakim Bruslund} and Rikke Gade and Moeslund, {Thomas B.} and Niels Madsen",
year = "2019",
month = "6",
language = "English",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition Workshops",

}

Detection of Marine Animals in a New Underwater Dataset with Varying Visibility. / Pedersen, Malte; Haurum, Joakim Bruslund; Gade, Rikke; Moeslund, Thomas B.; Madsen, Niels.

IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - Detection of Marine Animals in a New Underwater Dataset with Varying Visibility

AU - Pedersen, Malte

AU - Haurum, Joakim Bruslund

AU - Gade, Rikke

AU - Moeslund, Thomas B.

AU - Madsen, Niels

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

M3 - Article in proceeding

BT - IEEE Conference on Computer Vision and Pattern Recognition Workshops

ER -