An aquaculture-based binary classifier for fish detection using multilayer artificial neural network
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Conference Proceeding
Source Title
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019
Publication Date
11-1-2019
Abstract
Fish detection, a specific task in computer vision system for fish monitoring, is challenging due to the complex characteristics of the captured images. A proposed approach in tackling this challenging task was to incorporate a multilayer artificial neural network to a computer vision system algorithm, implemented in aquaculture. This computer vision system algorithm captured the images from the aquaculture setup. Then, these captured images were processed. After that, the features out of these processed images were extracted and utilized to develop this multilayer artificial neural network. The best configuration, which is trained with the least learning time and tested with least mean square error and highest accuracy, was determined by adjusting the number of neurons in the two hidden layers. The multilayer artificial neural network with 50 neurons in the first hidden layer and 10 neurons in the second layer was considered the best configuration; it has achieved learning time of 3.374 ms, mean square error of 0.2315, and accuracy of 79.00%, hence, proving the competitiveness of this approach. © 2019 IEEE.
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Digitial Object Identifier (DOI)
10.1109/HNICEM48295.2019.9072911
Recommended Citation
Almero, V., Concepcion, R., Rosales, M., Vicerra, R. P., Bandala, A. A., & Dadios, E. P. (2019). An aquaculture-based binary classifier for fish detection using multilayer artificial neural network. 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019 https://doi.org/10.1109/HNICEM48295.2019.9072911
Disciplines
Manufacturing
Keywords
Fishes—Detection; Fishes—Classification; Computer vision; Neural networks (Computer science)
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