Browse > Article
http://dx.doi.org/10.14372/IEMEK.2022.17.3.177

Automatic Fish Size Measurement System for Smart Fish Farm Using a Deep Neural Network  

Lee, Yoon-Ho (Pukyong National University)
Jeon, Joo-Hyeon (Pukyong National University)
Joo, Moon G. (Pukyong National University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.17, no.3, 2022 , pp. 177-183 More about this Journal
Abstract
To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.
Keywords
Deep neural network; YOLOv3; Object detection; Fish size; Smart fish farm; LabVIEW;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 D. E. Jeong, S. J. Park, H. J. Jeong, S. W. Son, H. L. Ko, T. H. Im, "A Study on Water Quality Data Collection and Utilization of Machine Learning Technique in Smart Aquaculture," Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, pp. 847-848, 2019 (in Korean).
2 Y. H. Lee, J. H. Jeon, M. G. Joo, B. H. Yoo, "Automatic Fish Size Measurement for Smart Fish Farm Using Deep Neural Network," Proceedings of IEMEK, fall, pp. 80-81, 2021 (in Korean).
3 X. Li, M. Shang, H. Qin, L. Chen, "Fast Accurate Fish Detection and Recognition of Underwater Images with Fast R-CNN," OCEANS 2015 - MTS/IEEE Washington, pp. 1-5, 2015.
4 J. Redmon, A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv preprint arXiv:1804.02767, 2018.
5 S. I. Hwang, O. Y. Kim, S. Y. Lee, "A Case Study on the ICT-Based Smart Aquaculture System by Applying u-Farms," The Journal of Korean Institute of Communications and Information Sciences, Vol. 39, No. 2, pp. 173-181, 2014 (in Korean).
6 J. H. Jeon, Y. H. Lee, N. E. Lee, M. G. Joo, "An Implementation of System for Control of Dissolved Oxygen and Temperature in the pools of Smart Fish Farm," IEMEK J. Embed. Sys. Appl., Vol. 16, No. 6, pp. 299-305, 2021 (in Korean).   DOI
7 G. Chen, P. Sun, Y. Shang, "Automatic Fish Classification System Using Deep Learning," 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, pp. 24-29, 2017.
8 J. H. Jeon, N. E. Lee, Y. H. Lee, J. M. Jang, M. G. Joo, "An Implementation of DAQ and Monitoring System for a Smart Fish Farm Using Circulation Filtration System," Journal of Information Processing Systems, Vol. 17, No. 6, pp. 1179-1190, 2021.   DOI
9 Y. H. Shin, J. H. Choi, H. S. Choi, "Deep Learning based Fish Object Detection and Tracking for Smart Aqua Farm," Journal of the Korea Contents Association, Vol. 21, No. 1, pp. 552-560, 2021 (in Korean).   DOI
10 M. Sung, S. Yu, Y. Girdhar, "Vision Based Real-time Fish Detection Using Convolutional Neural Network," OCEANS 2017 - Aberdeen, pp. 1-6, 2017.
11 W. Xu, S. Matzner, "Underwater Fish Detection Using Deep Learning for Water Power Applications," 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 313-318, 2018.
12 Q. C. Mao, H. M. Sun, Y. B. Liu, R. S. Jia, " Mini-YOLOv3: real-time object detector for embedded applications," Ieee Access, Vol. 7, pp. 133529-133538, 2019.   DOI