ArticleJournal of fish biology2024
Novel use of deep neural networks on photographic identification of epaulette sharks (Hemiscyllium ocellatum) across life stages.
Article in Journal of fish biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A wild fish image dataset for individual re-identification and phenotyping.Scientific data · 2026Article
- The ideal candidate for the job: how epaulette sharks (Conservation physiology · 2026Article
- Ontogenetic shifts in morphology and ecology of eastern Pacific white sharks revealed by computer vision.PloS one · 2026Article
- A blacktip's black tip: The reliability of using dorsal-fin patterns for photo identification of blacktip reef sharks (Carcharhinus melanopterus).Journal of fish biology · 2025Article
- Catch Data Can Unravel Elasmobranch Aggregation Dynamics and Group Behaviours.Ecology and evolution · 2025Article
- Novel use of deep neural networks on photographic identification of epaulette sharks (Hemiscyllium ocellatum) across life stages.Journal of fish biology · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Photographic identification (photo ID) is an established method that is used to count animals and track individuals' movements. This method performs well with some species of elasmobranchs (i.e., sharks, skates, and rays) where individuals have distinctive skin patterns. However, the unique skin patterns used for ID must be stable through time to allow re-identification of individuals in future sampling events. More recently, artificial intelligence (AI) models have substantially decreased the labor-intensive process of matching photos in extensive photo ID libraries and increased the reliability of photo ID. Here, photo ID and AI are used for the first time to identify epaulette sharks (Hemiscyllium ocellatum) at different life stages for approximately 2 years. An AI model was developed to assess and compare the reliability of human-classified ID patterns in juvenile and neonate sharks. The model also tested the persistence of unique patterns in adult sharks. Results indicate that immature life stages are unreliable for pattern identification, using both human and AI approaches, due to the plasticity of these subadult growth forms. Mature sharks maintain their patterns through time and can be identified by AI models with approximately 86% accuracy. The approach outlined in this study has the potential of validating the stability of ID patterns through time; however, testing on wild populations and long-term datasets is needed. This study's novel deep neural network development strategy offers a streamlined and accessible framework for generating a reliable model from a small data set, without requiring high-performance computing. Since many photo ID studies commence with limited datasets and resources, this AI model presents practical solutions to such constraints. Overall, this approach has the potential to address challenges associated with long-term photo ID data sets and the application of AI for shark identification.
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