JACIII Vol.26 No.5 pp. 851-858
doi: 10.20965/jaciii.2022.p0851


Low-Cost Underwater Camera: Design and Development

Elmer P. Dadios*,**,†, Vincent Jan Almero***, Ronnie S. Concepcion II*,**, Ryan Rhay P. Vicerra*,**, Argel A. Bandala**,***, and Edwin Sybingco**,***

*Department of Manufacturing Engineering and Management, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

**Center for Engineering and Sustainability Development Research, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

***Department of Electronics and Computer Engineering, De La Salle University (DLSU)
2401 Taft Avenue, Malate, Manila 1004, Philippines

Corresponding author

May 5, 2022
July 15, 2022
September 20, 2022
additive manufacturing, computational intelligence, fish monitoring, underwater camera

The understanding of vision-based data acquisition and processing aids in developing predictive frameworks and decision support systems for efficient aquaculture monitoring and management. However, this emerging field is confronted by a lack of high-quality underwater visual data, whether from public or local setups and high cost of development. In this regard, an underwater camera that captures underwater images from an inland freshwater aquaculture setup was proposed. The components of the underwater camera system are primarily based on Raspberry Pi, an open-source computing platform. The underwater camera continuously provides a real-time video streaming link of underwater scenes, and the local processor periodically acquires and stores data from this link in the form of images. These data are stored locally and remotely. Based on the results of the developed low-cost underwater camera, it captures and differentiate fish region to its background before and after flushing as influenced by turbidity. Hence, the developed camera can be used for both aquarium and inland aquaculture pond setup for fish monitoring.

Exploded view of RPi-based underwater camera

Exploded view of RPi-based underwater camera

Cite this article as:
E. Dadios, V. Almero, R. Concepcion II, R. Vicerra, A. Bandala, and E. Sybingco, “Low-Cost Underwater Camera: Design and Development,” J. Adv. Comput. Intell. Intell. Inform., Vol.26 No.5, pp. 851-858, 2022.
Data files:
  1. [1] F. Antonucci and C. Costa, “Precision aquaculture: a short review on engineering innovations,” Aquaculture Int., Vol.28, pp. 41-57, doi: 10.1007/s10499-019-00443-w, 2020.
  2. [2] J. Radinger, J. R. Britton, S. M. Carlson et al., “Effective monitoring of freshwater fish,” Fish and Fisheries, Vol.20, No.4, pp. 729-747, doi: 10.1111/faf.12373, 2019.
  3. [3] M. Saberioon, A. Gholizadeh, P. Cisar, A. Pautsina, and J. Urban, “Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues,” Reviews in Aquaculture, Vol.9, No.4, pp. 369-387, 2016.
  4. [4] V. M. Papadakis, I. E. Papadakis, F. Lamprianidou, A. Glaropoulos, and M. Kentouri, “A computer-vision system and methodology for the analysis of fish behavior,” Aquacultural Engineering, Vol.46, pp. 53-59, doi: 10.1016/J.AQUAENG.2011.11.002, 2012.
  5. [5] X. Yang, S. Zhang, J. Liu, Q. Gao, S. Dong, and C. Zhou, “Deep learning for smart fish farming: applications, opportunities and challenges,” Reviews in Aquaculture, Vol.13, No.1, pp. 66-90, doi: 10.1111/raq.12464, 2020.
  6. [6] M. Føre, K. Frank, T. Norton et al., “Precision fish farming: A new framework to improve production in aquaculture,” Biosystems Engineering, Vol.173, pp. 176-193, doi: 10.1016/j.biosystemseng.2017.10.014, 2018.
  7. [7] G. Hou, J. Li, G. Wang, H. Yang, B. Huang, and Z. Pan, “A novel dark channel prior guided variational framework for underwater image restoration,” J. of Visual Communication and Image Representation, Vol.66, 102732, doi: 10.1016/j.jvcir.2019.102732, 2020.
  8. [8] L. Yang, Y. Liu, H. Yu, X. Fang, L. Song, D. Li, and Y. Chen, “Computer Vision Models in Intelligent Aquaculture with Emphasis on Fish Detection and Behavior Analysis: A Review,” Archives of Computational Methods in Engineering, Vol.28, pp. 2785-2816, doi: 10.1007/s11831-020-09486-2, 2020.
  9. [9] R. Pettersen, H. L. Braa, B. A. Gawel, P. A. Letnes, K. Sæther, and L. M. S. Aas, “Detection and classification of Lepeophterius salmonis (Krøyer, 1837) using underwater hyperspectral imaging,” Aquacultural Engineering, Vol.87, No.9, 102025, doi: 10.1016/j.aquaeng.2019.102025, 2019.
  10. [10] A. Schneider and H. Feussner, “Diagnostic procedures; nuclear imaging systems,” Biomedical Engineering in Gastrointestinal Surgery, pp. 87-220, doi: 10.1016/B978-0-12-803230-5.00005-1, 2017.
  11. [11] C. Damian, D. Grigorescu, I. Ghinda, and M. Robu, “Using Mono and Stereo Camera System for Static and Moving Objects Detection,” Proc. of the 2019 Int. Conf. on Electromechanical and Energy Systems (SIELMEN 2019), pp. 1-5, doi: 10.1109/SIELMEN.2019.8905820, 2019.
  12. [12] A. M. Chaudhry, M. M. Riaz, and A. Ghafoor, “Underwater visibility restoration using dehazing, contrast enhancement and filtering,” Multimedia Tools and Applications, Vol.78, pp. 28179-28187, doi: 10.1007/s11042-019-07922-5, 2019.
  13. [13] M. Jian, Q. Qi, H. Yu et al., “The extended marine underwater environment database and baseline evaluations,” Applied Soft Computing J., Vol.80, pp. 425-437, doi: 10.1016/j.asoc.2019.04.025, 2019.
  14. [14] M. Hou, R. Liu, X. Fan, and Z. Luo, “Joint Residual Learning for Underwater Image Enhancement,” Proc. of the 2018 25th IEEE Int. Conf. on Image Processing (ICIP), pp. 4043-4047, doi: 10.1109/ICIP.2018.8451209, 2018.
  15. [15] Y. Zhang, F. Yang, and W. He, “An approach for underwater image enhancement based on color correction and dehazing,” Int. J. of Advanced Robotic Systems, Vol.17, No.5, pp. 1-10, doi: 10.1177/1729881420961643, 2020.
  16. [16] V. J. Almero, R. Concepcion, M. Rosales, R. R. Vicerra, A. Bandala, and E. Dadios, “An Aquaculture-Based Binary Classifier for Fish Detection Using Multilayer Artificial Neural Network,” IEEE 11th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM48295.2019.9072911, 2019.
  17. [17] V. J. D. Almero, J. D. Alejandrino, A. A. Bandala, and E. P. Dadios, “Segmentation of Aquaculture Underwater Scene Images Based on SLIC Superpixels Merging-Fast Marching Method Hybrid,” Proc. of the 2020 IEEE Region 10 Conf. (TENCON), pp. 432-437, 2020.
  18. [18] V. J. D. Almero, R. I. S. Concepcion, J. D. Alejandrino, A. A. Bandala, and E. P. Dadios, “Particle Swarm Optimization-Based Dark Channel Prior Parameters Selection for Single Underwater Image Dehazing,” Proc. of the 9th Int. Symp. on Computational Intelligence and Industrial Applications (ISCIIA), pp. 1-8, 2020.
  19. [19] V. J. D. Almero, R. I. S. Concepcion, J. D. Alejandrino et al., “Genetic Algorithm-Based Dark Channel Prior Parameters Selection for Single Underwater Image Dehazing,” Proc. of the 2020 IEEE (TENCON), pp. 1153-1158, 2020.
  20. [20] V. J. D. Almero, R. S. Concepcion II, E. Sybingco, and E. P. Dadios, “An Image Classifier for Underwater Fish Detection Using Classification Tree-Artificial Neural Network Hybrid,” Proc. of the 2020 RIVF Int. Conf. on Computing and Communication Technologies, pp. 1-6, doi: 10.1109/rivf48685.2020.9140795, 2020.
  21. [21] J. A. Bergshoeff, N. Zargarpour, G. Legge, B. Favaro, and S. Johannessen, “How to build a low-cost underwater camera housing for aquatic research,” FACETS, Vol.2, No.1, pp. 150-159, doi: 10.1139/facets-2016-0048, 2017.
  22. [22] X. Mouy, M. Black, K. Cox, J. Qualley, C. Mireault, S. Dosso, and F. Juanes, “FishCam: A low-cost open source autonomous camera for aquatic research,” HardwareX, Vol.8, doi: 10.1016/j.ohx.2020.e00110, 2020.

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Last updated on Jul. 12, 2024