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A Deep Learning Model to Recognize and Quantitatively Analyze Cold Seep Substrates and the Dominant Associated Species

Authors :
Haining Wang
Xiaoxue Fu
Chengqian Zhao
Zhendong Luan
Chaolun Li
Source :
Frontiers in Marine Science, Vol 8 (2021)
Publication Year :
2021
Publisher :
Frontiers Media S.A., 2021.

Abstract

Characterizing habitats and species distribution is important to understand the structure and function of cold seep ecosystems. This paper develops a deep learning model for the fast and accurate recognition and classification of substrates and the dominant associated species in cold seeps. Considering the dense distribution of the dominant associated species and small objects caused by overlap in cold seeps, the feature pyramid network (FPN) embed into the faster region-convolutional neural network (R-CNN) was used to detect large-scale changes and small missing objects without increasing the number of calculations. We applied three classifiers (Faster R-CNN + FPN for mussel beds, lobster clusters and biological mixing, CNN for shell debris and exposed authigenic carbonates, and VGG16 for reduced sediments and muddy bottom) to improve the recognition accuracy of substrates. The model’s results were manually verified using images obtained in the Formosa cold seep during a 2016 cruise. The recognition accuracy of the two dominant species, e.g., Gigantidas platifrons and Munidopsidae could be 70.85 and 56.16%, respectively. Seven subcategories of substrates were also classified with a mean accuracy of 74.87%. The developed model is a promising tool for the fast and accurate characterization of substrates and epifauna in cold seeps, which is crucial for large-scale quantitative analyses.

Details

Language :
English
ISSN :
22967745
Volume :
8
Database :
Directory of Open Access Journals
Journal :
Frontiers in Marine Science
Publication Type :
Academic Journal
Accession number :
edsdoj.6fba8cbba4324a17888f93f7fa5541c4
Document Type :
article
Full Text :
https://doi.org/10.3389/fmars.2021.775433