Deep learning-based image classification of turtles imported into Korea

Baek, Jong-Won; Kim, Jung-Il; Kim, Chang-Bae

doi:10.1038/s41598-023-49022-3

Cited by 3 publications

References 53 publications

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Automated classification in turtles genus Malayemys using ensemble multiview image based on improved YOLOv8 with CNN

Songpan,

Chaianunporn,

Lomthaisong

et al. 2024

Sci Rep

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In Thailand, two snail-eating turtle species in the genus Malayemes (M. subtrijuga and M. macrocephala) are protected animals in which smuggling and trading are illegal. Recently, a new species M. khoratensis has been reported and it has not yet been considered as protected animal species. To enforce the law, species identification of Malayemes is crucial. However, it is quite challenging and requires expertise. Therefore, a simple tool, such as image analysis, to differentiate these three snail-eating species would be highly useful. This study proposes a novel ensemble multiview image processing approach for the automated classification of three turtle species in the genus Malayemys. The original YOLOv8 architecture was improved by utilizing a convolutional neural network (CNN) to overcome the limitations of traditional identification methods. This model captures unique morphological features by analyzing Malayemys species images from various angles, addressing challenges such as occlusion and appearance variations. The ensemble multiview strategy significantly increases the YOLOv8 classification accuracy using a comprehensive dataset, achieving an average mean average precision (mAP) of 98% for the genus Malayemys compared with the nonensemble multiview and single-view strategies. The species identification accuracy of the proposed models was validated by comparing genetic methods using mitochondrial DNA with morphological characteristics. Even though the morphological characteristics of these three species are ambiguous, the mitochondrial DNA sequences are quite distinct. Therefore, this alternative tool should be used to increase confidence in field identification. In summary, the contribution of this study not only marks a significant advancement in computational biology but also supports wildlife and turtle conservation efforts by enabling rapid, accurate species identification.

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Automated classification in turtles genus Malayemys using ensemble multiview image based on improved YOLOv8 with CNN

Songpan,

Chaianunporn,

Lomthaisong

et al. 2024

Sci Rep

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Enhanced Classification of Reptiles and Amphibians Using Deep Learning: A Comparative Analysis of Model Performance

Kaushik,

Khurana

2024

2024 International Conference on Cybernation and Computation (CYBERCOM)

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Deep learning-based image classification of sea turtles using object detection and instance segmentation models

Baek,

Kim,

Kim

2024

PLoS ONE

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Sea turtles exhibit high migratory rates and occupy a broad range of habitats, which in turn makes monitoring these taxa challenging. Applying deep learning (DL) models to vast image datasets collected from citizen science programs can offer promising solutions to overcome the challenge of monitoring the wide habitats of wildlife, particularly sea turtles. Among DL models, object detection models, such as the You Only Look Once (YOLO) series, have been extensively employed for wildlife classification. Despite their successful application in this domain, detecting objects in images with complex backgrounds, including underwater environments, remains a significant challenge. Recently, instance segmentation models have been developed to address this issue by providing more accurate classification of complex images compared to traditional object detection models. This study compared the performance of two state-of-the-art DL methods namely; the object detection model (YOLOv5) and instance segmentation model (YOLOv5-seg), to detect and classify sea turtles. The images were collected from iNaturalist and Google and then divided into 64% for training, 16% for validation, and 20% for test sets. Model performance during and after finishing training was evaluated by loss functions and various indexes, respectively. Based on loss functions, YOLOv5-seg demonstrated a lower error rate in detecting rather than classifying sea turtles than the YOLOv5. According to mean Average Precision (mAP) values, which reflect precision and recall, the YOLOv5-seg model showed superior performance than YOLOv5. The mAP0.5 and mAP0.5:0.95 for the YOLOv5 model were 0.885 and 0.795, respectively, whereas for the YOLOv5-seg, these values were 0.918 and 0.831, respectively. In particular, based on the loss functions and classification results, the YOLOv5-seg showed improved performance for detecting rather than classifying sea turtles compared to the YOLOv5. The results of this study may help improve sea turtle monitoring in the future.

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Deep learning-based image classification of turtles imported into Korea

Cited by 3 publications

References 53 publications

Automated classification in turtles genus Malayemys using ensemble multiview image based on improved YOLOv8 with CNN

Automated classification in turtles genus Malayemys using ensemble multiview image based on improved YOLOv8 with CNN

Enhanced Classification of Reptiles and Amphibians Using Deep Learning: A Comparative Analysis of Model Performance

Deep learning-based image classification of sea turtles using object detection and instance segmentation models

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