An Automated Framework Based on Deep Learning for Shark Recognition

Published on
09 July 2022

An Automated Framework Based on Deep Learning for Shark Recognition

Nhat Anh Le, Jucheol Moon, Christopher G. Lowe, Hyun-Il Kim, Sang-Il Choi


The recent progress in deep learning has given rise to a non-invasive and effective approach for animal biometrics. These modern techniques allow researchers to track animal individuals on a large-scale image database. Typical approaches are suited to a closed-set recognition problem, which is to identify images of known objects only. However, such approaches are not scalable because they mis-classify images of unknown objects. To recognize the images of unknown objects as ‘unknown’, a framework should be able to deal with the open set recognition scenario. This paper proposes a fully automatic, vision-based identification framework capable of recognizing shark individuals including those that are unknown. The framework first detects and extracts the shark from the original image. After that, we develop a deep network to transform the extracted image to an embedding vector in latent space. The proposed network consists of the Visual Geometry Group-UNet (VGG-UNet) and a modified Visual Geometry Group-16 (VGG-16) network. The VGG-UNet is utilized to detect shark bodies, and the modified VGG-16 is used to learn embeddings of shark individuals. For the recognition task, our framework learns a decision boundary using a one-class support vector machine (OSVM) for each shark included in the training phase using a few embedding vectors belonging to them, then it determines whether a new shark image is recognized as belonging to a known shark individual. Our proposed network can recognize shark individuals with high accuracy and can effectively deal with the open set recognition problem with shark images.

J. Mar. Sci. Eng. 2022, 10, 942. DOI: 10.3390/jmse10070942


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