Abstract
Fish detection and identification are important steps towards monitoring fish behavior. The importance of such monitoring step comes from the need for better understanding of the fish ecology and issuing conservative actions for keeping the safety of this vital food resource. The recent advances in machine learning approaches allow many applications to easily analyze and detect a number of fish species. The main competence between these approaches is based on two main detection parameters: the time and the accuracy measurements. Therefore, this paper proposes a fish detection approach based on BAT optimization algorithm (BA). This approach aims to reduce the classification time within the fish detection process. The performance of this system was evaluated by a number of well-known machine learning classifiers, KNN, ANN, and SVM. The approach was tested with 151 images to detect the Nile Tilapia fish species and the results showed that k-NN can achieve high accuracy 90 %, with feature reduction ratio close to 61 % along with a noticeable decrease in the classification time.
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Acknowledgments
This paper has been elaborated in the framework of the project New creative teams in priorities of scientific research, reg. no. CZ.1.07/2.3.00/30.0055, supported by Operational Programme Education for Competitiveness and co-financed by the European Social Fund and the state budget of the Czech Republic and supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme. This work was partially supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement No. 316555. This fund only apply for one author (Hossam M. Zawbaa). Also, we wish to acknowledge the efforts of Rehab Adly Shehabeldin who supports in the data set collection process.
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Fouad, M.M., Zawbaa, H.M., Gaber, T., Snasel, V., Hassanien, A.E. (2016). A Fish Detection Approach Based on BAT Algorithm. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_25
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DOI: https://doi.org/10.1007/978-3-319-26690-9_25
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