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Article

Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network

1
Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
2
Mahjung Aquabest Hatchery, Lot 9569 Kampung Batu 4, Segari 32200, Malaysia
*
Authors to whom correspondence should be addressed.
Academic Editors: Celestine Iwendi and Thippa Reddy Gadekallu
Water 2022, 14(2), 222; https://doi.org/10.3390/w14020222
Received: 30 November 2021 / Revised: 4 January 2022 / Accepted: 4 January 2022 / Published: 12 January 2022
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
Fish production has become a roadblock to the development of fish farming, and one of the issues encountered throughout the hatching process is the counting procedure. Previous research has mainly depended on the use of non-machine learning-based and machine learning-based counting methods and so was unable to provide precise results. In this work, we used a robotic eye camera to capture shrimp photos on a shrimp farm to train the model. The image data were classified into three categories based on the density of shrimps: low density, medium density, and high density. We used the parameter calibration strategy to discover the appropriate parameters and provided an improved Mask Regional Convolutional Neural Network (Mask R-CNN) model. As a result, the enhanced Mask R-CNN model can reach an accuracy rate of up to 97.48%. View Full-Text
Keywords: deep learning; counting; shrimp detection; underwater fish; machine learning deep learning; counting; shrimp detection; underwater fish; machine learning
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MDPI and ACS Style

Hong Khai, T.; Abdullah, S.N.H.S.; Hasan, M.K.; Tarmizi, A. Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network. Water 2022, 14, 222. https://doi.org/10.3390/w14020222

AMA Style

Hong Khai T, Abdullah SNHS, Hasan MK, Tarmizi A. Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network. Water. 2022; 14(2):222. https://doi.org/10.3390/w14020222

Chicago/Turabian Style

Hong Khai, Teh, Siti N.H.S. Abdullah, Mohammad K. Hasan, and Ahmad Tarmizi. 2022. "Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network" Water 14, no. 2: 222. https://doi.org/10.3390/w14020222

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