Fish Image Classification Using Adaptive Learning Rate In Transfer Learning Method

Rizka Suhana, Wayan Firdaus Mahmudy, Agung Setia Budi


The existence of fish species diversity in coastal ecosystems which include mangrove forests, seagrass beds and coral reefs is one of the benchmarks in determining health in coastal ecosystems. It is certain that we must maintain, preserve and care for so that conservation efforts need to be carried out in water areas. Many experts at the Indonesian Fisheries and Marine Research and Development Agency often classify fish images manually, of course it will take a long time, therefore with today's developments they can use the latest technology.  One of the reliable techniques in terms of image classification is Convolutional Neural Network (CNN). As time goes by, of course, many people want fast learning and solving new problems faster and better, so transfer learning appears, which adopts part of CNN, the name is modified convolution layer. Observing the needs of experts in the field of marine conservation, the researchers decided to solve this problem by using transfer learning modifications. The transfer learning used is an architectural model from the pre-trained Mobilenet V2, which is known for its light computing process and can be applied to our gadgets and other embedded tools. The research image data used is 49.281 data of various sizes and there are 18 types of fish, in the pre-processing data there is a resize of the image to a size of 224x224 pixels. testing with the modified transfer learning architectural model obtained an accuracy score of 99.54%, this model is quite reliable in classifying fish images.

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A. Dermawan, S. Bahri Lubris, D. Sadili, A. Kasasiah, and F. Agung Kunto Kurniawan, 14th Konservasi Untuk Kesejahteraan, vol. 7, no. 2. 2014.

E. Yuliana, M. Boer, A. Fahrudin, and M. M. Kamal, “Biofiversitas Ikan Karang di kawasan Konservasi Taman Nasional Karimunjwa,” J. Ilmu dan Teknol. Kelaut. Trop., vol. 9, no. 1, pp. 29–43, 2017.

O. M. Luthfi et al., “Pemantauan Kondisi Ikan Karang Menggunakan Metode Reef Check Di Perairan Selat Sempu Malang Selatan,” J. Mar. Aquat. Sci., vol. 3, no. 2, p. 171, 2017.

B. J. Boom et al., “Long-term underwater camera surveillance for monitoring and analysis of fish populations,” Work. Vis. Obs. Anal. Anim. Insect Behav. (VAIB), conjunction with ICPR 2012, no. August 2015, pp. 2–5, 2012.

S. Villon et al., “A Deep learning method for accurate and fast identification of coral reef fishes in underwater images,” Ecol. Inform., vol. 48, no. August, pp. 238–244, 2018.

D. Siswanto, D. Syauqy, and A. S. Budi, “Sistem Klasifikasi Ikan Tongkol yang Mengandung Formalin dengan Sensor HCHO dan Sensor pH Menggunakan Metode K-Nearest Neighbor Berbasis Arduino,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 10, pp. 9993–9997, 2019.

L. Muflikhah, W. Widodo, W. F. Mahmudy, and S. Solimun, “A support vector machine based on kernel k-means for detecting the liver cancer disease,” Int. J. Intell. Eng. Syst., vol. 13, no. 3, pp. 293–303, 2020.

F. A. I. Achyunda Putra, F. Utaminingrum, and W. F. Mahmudy, “HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 3, p. 231, 2020.

D. M. N. Fajri, W. F. Mahmudy, and T. Yulianti, “Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network,” J. Inf. Technol. Comput. Sci., vol. 6, no. 1, p. 18, 2021.

N. F. F. Alshdaifat, A. Z. Talib, and M. A. Osman, “Improved deep learning framework for fish segmentation in underwater videos,” Ecol. Inform., vol. 59, no. May, p. 101121, 2020.

S. Cui, Y. Zhou, Y. Wang, and L. Zhai, “Fish Detection Using Deep Learning,” Appl. Comput. Intell. Soft Comput., vol. 2020, 2020.

B. S. Rekha, G. N. Srinivasan, S. K. Reddy, D. Kakwani, and N. Bhattad, Fish detection and classification using convolutional neural networks, vol. 1108 AISC, no. July. Springer International Publishing, 2020.

F. Kratzert and H. Mader, “Fish species classification in underwater video monitoring using Convolutional Neural Networks,” 2018.

D. Li, Z. Wang, S. Wu, Z. Miao, L. Du, and Y. Duan, “Automatic recognition methods of fish feeding behavior in aquaculture: A review,” Aquaculture, vol. 528, p. 735508, 2020.

A. G. Howard et al., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2017.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 4510–4520, 2018.

B. J. Boom, P. X. Huang, J. He, and R. B. Fisher, “Supporting ground-truth annotation of image datasets using clustering,” Proc. - Int. Conf. Pattern Recognit., no. January, pp. 1542–1545, 2012.

L. N. Smith, “Cyclical learning rates for training neural networks,” Proc. - 2017 IEEE Winter Conf. Appl. Comput. Vision, WACV 2017, no. April, pp. 464–472, 2017.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning: Machine Learning Book. 2016.



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