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Bottlenose dolphin identification using synthetic image-based transfer learning
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DOI：10.1016/j.ecoinf.2024.102909.png)
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Abstract
En 中文
The Indo-Pacific bottlenose dolphin (IPBD) (Tursiops aduncus) is a key species in marine ecosystems. Photoidentification (photo-ID) is a fundamental method for studying dolphin populations by identifying individuals based on the unique features of their dorsal fins. Despite recent developments in learning-based photo-ID algorithms, the lack of training data for these models has become a bottleneck for improving the accuracy of these algorithms. In this study, we used synthetic image generation and deep learning to improve photography-based IPBD identification. We generated 7500 synthetic dorsal fin images of 30 dolphins and trained a custom triplet neural network using ResNet50 to distinguish individuals. The model achieved 84.8 % accuracy within the top 10-ranked positions and 72.2 % accuracy in the top 5-ranked positions, demonstrating the potential of these technologies to enhance IPBD monitoring and conservation efforts.
Keywords:
Indo-Pacific bottlenose dolphin
Individual identification
Photo-ID
Synthetic image
Transfer learning
Triplet neural network
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