ArticleScientific reports2023
Deep learning-based image classification of turtles imported into Korea.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- From paper to pixels - digitized maps of vascular plant distributions in Denmark in the early 20th century.Scientific data · 2026Article
- Hyperparameter optimization to enhance the performance of deep learning models for the early detection of invasive turtles in Korea.Scientific reports · 2026Article
- Hierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots.Scientific reports · 2025Article
- Automated classification in turtles genus Malayemys using ensemble multiview image based on improved YOLOv8 with CNN.Scientific reports · 2024Article
- Deep learning-based image classification of sea turtles using object detection and instance segmentation models.PloS one · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Although turtles play a key role in maintaining healthy and balanced environments, these are endangered due to global trade to meet the high demand for food, medicine, and pets in Asia. In addition, imported non-native turtles have been controlled as alien invasive species in various countries, including Korea. Therefore, a rapid and accurate classification of imported turtles is needed to conserve and detect those in native ecosystems. In this study, eight Single Shot MultiBox Detector (SSD) models using different backbone networks were used to classify 36 imported turtles in Korea. The images of these species were collected from Google and were identified using morphological features. Then, these were divided into 70% for training, 15% for validation, and 15% for test sets. In addition, data augmentation was applied to the training set to prevent overfitting. Among the eight models, the Resnet18 model showed the highest mean Average Precision (mAP) at 88.1% and the fastest inference time at 0.024 s. The average correct classification rate of 36 turtles in this model was 82.8%. The results of this study could help in management of the turtle trade, specifically in improving detection of alien invasive species in the wild.
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Registered trials
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