Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks
Revealing Sea Turtle Behavior in Relation to Fishing Gear Using Color-Coded Spatiotemporal Motion Patterns With Deep Neural Networks
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DOI：
10.3389/fmars.2021.785357
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
J. Reavis;H. S. Demir;B. Witherington;Michael J. Bresette;Jennifer Blain Christen;J. Senko;S. Ozev
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文献类型：
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作者：
J. Reavis;H. S. Demir;B. Witherington;Michael J. Bresette;Jennifer Blain Christen;J. Senko;S. Ozev
海洋物种的附带捕获或兼捕是一个全球性的保护问题。与渔具的相互作用可能导致包括海龟在内的呼吸空气的海洋巨型动物死亡。尽管如此，海龟和渔具之间的相互作用-从行为的角度来看-在文献中没有充分的记录或描述。了解海龟与渔具的关系是发现它们如何被渔具缠绕或诱捕的关键。这一信息也可用于减少渔业相互作用。然而，记录和分析这些行为是困难和耗时的。在这项研究中，我们提出了一个基于机器学习的海龟行为识别方案。该方法利用视觉目标跟踪和方向估计任务提取的重要特征，用于识别感兴趣的行为与绿色海龟（Chelonia mydas）作为研究对象。然后，将这些特征组合在一个颜色编码的特征图像中，该图像表示在有限的时间范围内发生的海龟行为。这些时空特征图像沿着深度卷积神经网络模型来识别所需的行为，特别是我们标记为“反转”和“掉头”的规避行为。实验结果表明，该方法在识别目标行为模式时，平均F1得分达到85%。这种方法旨在成为一种工具，以发现海龟为什么会被刺网渔具缠住。
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.