A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 09 2020
04 09 2020
Historique:
received:
18
11
2019
accepted:
14
08
2020
entrez:
5
9
2020
pubmed:
6
9
2020
medline:
20
3
2021
Statut:
epublish
Résumé
Visual analysis of complex fish habitats is an important step towards sustainable fisheries for human consumption and environmental protection. Deep Learning methods have shown great promise for scene analysis when trained on large-scale datasets. However, current datasets for fish analysis tend to focus on the classification task within constrained, plain environments which do not capture the complexity of underwater fish habitats. To address this limitation, we present DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 habitats in the marine-environments of tropical Australia. The dataset originally contained only classification labels. Thus, we collected point-level and segmentation labels to have a more comprehensive fish analysis benchmark. These labels enable models to learn to automatically monitor fish count, identify their locations, and estimate their sizes. Our experiments provide an in-depth analysis of the dataset characteristics, and the performance evaluation of several state-of-the-art approaches based on our benchmark. Although models pre-trained on ImageNet have successfully performed on this benchmark, there is still room for improvement. Therefore, this benchmark serves as a testbed to motivate further development in this challenging domain of underwater computer vision.
Identifiants
pubmed: 32887922
doi: 10.1038/s41598-020-71639-x
pii: 10.1038/s41598-020-71639-x
pmc: PMC7473859
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
14671Références
IEEE Trans Pattern Anal Mach Intell. 2017 Apr;39(4):640-651
pubmed: 27244717
Foods. 2017 Jul 19;6(7):
pubmed: 28753923