Abstract
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.
Highlights
Incidental capture of non-target animal species, termed bycatch, in fisheries is a global ecological threat to marine wildlife (Estes et al, 2011)
We propose a hybrid approach for the sea turtle behavior recognition task
We use Center Location Error (CLE) and Overlap Ratio (OR) as two base metrics which are widely used in object tracking problems (Wu et al, 2013)
Summary
Incidental capture of non-target animal species, termed bycatch, in fisheries is a global ecological threat to marine wildlife (Estes et al, 2011). Attempted solutions include: marine policy that sets bycatch limits for fisheries (Moore et al, 2009); acoustic deterrents similar to Revealing Sea Turtle Motion Patterns pingers used to prevent dolphin bycatch; buoyless nets and illuminated nets, which have shown promising results for reducing bycatch in coastal net fisheries (Wang et al, 2010; Peckham et al, 2016). These bycatch reduction approaches can involve changing the technical design of gear or introducing novel visual or acoustic stimuli, which changes gear configuration. With the developments in computer vision-based approaches, recognition of certain behaviors can be performed automatically after training this convolutional neural network with behavioral data
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