Siti Nurfarahim Shaharudin
ABSTRACT
Identification of individual turtles for population studies usually uses fin tags or other physical markers. Since this method is not very practical, we are studying different convolutional neural networks (CNN) to identify individual images of turtles by using transfer learning networks AlexNet, GoogleNet, VGG-19 Net and ResNet50. Sea turtles, unlike other animals, have unique facial patterns, making them excellent prospects for feature recognition. This study examined 1426 images of right facial scutes from 20 classes of turtles. Experiments with high-quality, low-quality and a mixture of both image qualities were conducted to test the performance of different CNNs. The ResNet50 technique achieved 95.3% accuracy with a mixed dataset, and AlexNet obtained the highest accuracy with high-quality image dataset (97.74%). The VGG19 network, on the other hand, performed well for dataset containing low-quality images with 91.82% accuracy. Experiments have shown different results for each data set. However, considering the uncertainty in a real underwater environment where the captured image quality is sometimes high and sometimes low, the ResNet50 network addresses this task-related problem as it has achieved the highest accuracy for the mixed dataset.
- Zafer Attal and Cem Direkoglu. 2019. Sea turtle species classification for environmental research and conservation. In International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions. Springer, 580–587.Google Scholar
- S Santhosh Baboo and ARJ Vigneswari. 2014. Identification of olive Ridley turtle using feature extraction. In 2014 International Conference on Intelligent Computing Applications. IEEE, 69–72.Google ScholarDigital Library
- Mohammed Brahimi, Kamel Boukhalfa, and Abdelouahab Moussaoui. 2017. Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence 31, 4 (2017), 299–315.Google ScholarDigital Library
- Steven JB Carter, Ian P Bell, Jessica J Miller, and Peter P Gash. 2014. Automated marine turtle photograph identification using artificial neural networks, with application to green turtles. Journal of experimental marine biology and ecology 452 (2014), 105–110.Google ScholarCross Ref
- Vicki Yii-Ching Chew, Hock-Chark Liew, and Juanita Joseph. 2015. Photographic identification of green turtles (Chelonia mydas) at Redang Island, Malaysia. Marine Turtle Newsletter 146 (2015), 1–6.Google Scholar
- Peter Dutton and Donna McDonald. 1994. Use of PIT tags to identify adult leatherbacks. Marine Turtle Newsletter 67 (1994), 13–14.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.Google ScholarCross Ref
- Jason Holmberg, Bradley Norman, and Zaven Arzoumanian. 2009. Estimating population size, structure, and residency time for whale sharks Rhincodon typus through collaborative photo-identification. Endangered Species Research 7, 1 (2009), 39–53.Google ScholarCross Ref
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2017. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 6 (2017), 84–90.Google ScholarDigital Library
- Jiangchuan Liu, Mantao Wang, Lie Bao, Xiaofan Li, Jun Sun, and Yue Ming. 2020. Classification and recognition of turtle images based on convolutional neural network. In IOP Conference Series: Materials Science and Engineering, Vol. 782. IOP Publishing, 052044.Google ScholarCross Ref
- Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22, 10 (2009), 1345–1359.Google ScholarDigital Library
- Júlia Reisser, Maíra Proietti, Paul Kinas, and Ivan Sazima. 2008. Photographic identification of sea turtles: method description and validation, with an estimation of tag loss. Endangered Species Research 5, 1 (2008), 73–82.Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).Google Scholar
- Navjot S Sodhi and Paul R Ehrlich. 2010. Conservation biology for all. Oxford University Press.Google Scholar
- C Szegedy, W Liu, Y Jia, P Sermanet, S Reed, D Anguelov, D Erhan, V Vanhoucke, and A Rabinovich. 2015. Going deeper with convolutions. In proceedings of the IEEE computer society conference on computer vision and pattern recognition.Google ScholarCross Ref
Index Terms
- Comparison of Convolutional Neural Network Architectures for Sea Turtle Individual's Recognition
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