Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks
| Autor: | Sule Ozev, Michael J. Bresette, H. Seckin Demir, Jennifer Blain Christen, Janie L. Reavis, Blair E. Witherington, Jesse Senko |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
spatiotemporal features neural network Computer science Science Fishing Ocean Engineering 02 engineering and technology QH1-199.5 Aquatic Science Oceanography color-coding 01 natural sciences Convolutional neural network GeneralLiterature_MISCELLANEOUS law.invention law 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 14. Life underwater Turtle (robot) Water Science and Technology Global and Planetary Change Chelonia mydas Artificial neural network biology business.industry 010604 marine biology & hydrobiology General. Including nature conservation geographical distribution Pattern recognition biology.organism_classification behavior recognition Bycatch Sea turtle Video tracking 020201 artificial intelligence & image processing Artificial intelligence green turtle business |
| Zdroj: | Frontiers in Marine Science, Vol 8 (2021) |
| ISSN: | 2296-7745 |
| DOI: | 10.3389/fmars.2021.785357 |
| Popis: | Incidental capture, or bycatch, of marine species is a global conservation concern. Interactions with fishing gear can cause mortality in air-breathing marine megafauna, including sea turtles. Despite this, interactions between sea turtles and fishing gear—from a behavior standpoint—are not sufficiently documented or described in the literature. Understanding sea turtle behavior in relation to fishing gear is key to discovering how they become entangled or entrapped in gear. This information can also be used to reduce fisheries interactions. However, recording and analyzing these behaviors is difficult and time intensive. In this study, we present a machine learning-based sea turtle behavior recognition scheme. The proposed method utilizes visual object tracking and orientation estimation tasks to extract important features that are used for recognizing behaviors of interest with green turtles (Chelonia mydas) as the study subject. Then, these features are combined in a color-coded feature image that represents the turtle behaviors occurring in a limited time frame. These spatiotemporal feature images are used along a deep convolutional neural network model to recognize the desired behaviors, specifically evasive behaviors which we have labeled “reversal” and “U-turn.” Experimental results show that the proposed method achieves an average F1 score of 85% in recognizing the target behavior patterns. This method is intended to be a tool for discovering why sea turtles become entangled in gillnet fishing gear. |
| Databáze: | OpenAIRE |
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