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An Approach for Counting Breeding Eels Using Mathematical Morphology Operations and Boundary Detection


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[1] N. T. Tuan, “An overview of the anguillid eel culture in Vietnam,” Journal of Aquaculture and Marine Biology, vol. 10, no. 3, pp. 96–101, Jun. 2021. https://doi.org/10.15406/jamb.2021.10.00312 Search in Google Scholar

[2] G. Farjon, O. Krikeb, A.B. Hillel, and V. Alchanatis, “Detection and counting of flowers on apple trees for better chemical thinning decisions,” Precision Agric, vol. 21, pp. 503–521, 2020. https://doi.org/10.1007/s11119-019-09679-1 Search in Google Scholar

[3] B. T. Kitano, C. C. T. Mendes, A. R. Geus, H. C. Oliveira, and J. R. Souza, “Corn plant counting using deep learning and UAV images,” IEEE Geoscience and Remote Sensing Letters, pp. 1–5, Aug. 2019. https://doi.org/10.1109/LGRS.2019.2930549 Search in Google Scholar

[4] M. Machefer, F. Lemarchand, V. Bonnefond, A. Hitchins, and P. Sidiropoulos, “Mask r-CNN refitting strategy for plant counting and sizing in UAV imagery,” Remote Sensing, vol. 12, no. 18, Art. no. 3015, Sep. 2020. https://doi.org/10.3390/rs12183015 Search in Google Scholar

[5] D. Li, Z. Miao, F. Peng, L. Wang, Y. Hao, Z. Wang, T. Chen, H. Li, and Y. Zheng, “Automatic counting methods in aquaculture: A review,” Journal of the World Aquaculture Society, vol. 52, no. 2, pp. 269–283, Apr. 2021. https://doi.org/10.1111/jwas.12745 Search in Google Scholar

[6] Q. Zhang, Y. Liu, C. Gong, Y. Chen, and H. Yu, “Applications of deep learning for dense scenes analysis in agriculture: A review,” Sensors, vol. 20, no. 5, Art. no. 1520, Mar. 2020. https://doi.org/10.3390/s20051520708550532164200 Search in Google Scholar

[7] Y. Zhong, J. Gao, Q. Lei, and Y. Zhou, “A vision-based counting and recognition system for flying insects in intelligent agriculture,” Sensors, vol. 18, no. 5, Art. no. 1489, May 2018. https://doi.org/10.3390/s18051489598214329747429 Search in Google Scholar

[8] Q.-J. Wang, S.-Y. Zhang, S.-F. Dong, G.-C. Zhang, J. Yang, R. Li, and H.-Q. Wang, “Pest24: A large-scale very small object data set of agricultural pests for multi-target detection,” Computers and Electronics in Agriculture, vol. 175, Art. no. 105585, Aug. 2020. https://doi.org/10.1016/j.compag.2020.105585 Search in Google Scholar

[9] M. Tian, H. Guo, H. Chen, Q. Wang, C. Long, and Y. Ma, “Automated pig counting using deep learning,” Computers and Electronics in Agriculture, vol. 163, Art. no. 104840, Aug. 2019. https://doi.org/10.1016/j.compag.2019.05.049 Search in Google Scholar

[10] S. Xie, R. Girshick, P. Dolla´r, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” arXiv, 2016. [Online]. Available: https://arxiv.org/abs/1611.0543110.1109/CVPR.2017.634 Search in Google Scholar

[11] W. Li, P. Chen, B. Wang, and C. Xie, “Automatic localization and count of agricultural crop pests based on an improved deep learning pipeline,” Scientific Reports, vol. 9, Art. no. 7024, May 2019. https://doi.org/10.1038/s41598-019-43171-0650493731065055 Search in Google Scholar

[12] E. A. Awalludin, T. N. T. Arsad, and W. N. J. H. W. Yussof, “A review on image processing techniques for fisheries application,” Journal of Physics: Conference Series, vol. 1529, no. 5, Art. no. 052031, May 2020. https://doi.org/10.1088/1742-6596/1529/5/052031 Search in Google Scholar

[13] D. Li and L. Du, “Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish,” Artificial Intelligence Review, vol. 55, pp. 4077–4116, Nov. 2021. https://doi.org/10.1007/s10462-021-10102-3 Search in Google Scholar

[14] J. Hu, D. Li, Q. Duan, Y. Han, G. Chen, and X. Si, “Fish species classification by color, texture and multi-class support vector machine using computer vision,” Computers and Electronics in Agriculture, vol. 88, pp. 133–140, Oct. 2012. https://doi.org/10.1016/j.compag.2012.07.008 Search in Google Scholar

[15] L. Li and J. Hong, “Identification of fish species based on image processing and statistical analysis research,” in 2014 IEEE International Conference on Mechatronics and Automation, Tianjin, China, Aug. 2014, pp. 1155–1160. https://doi.org/10.1109/ICMA.2014.6885861 Search in Google Scholar

[16] K. M. Knausgård, A. Wiklund, T. K. Sørdalen, K. T. Halvorsen, A. R. Kleiven, L. Jiao, and M. Goodwin, “Temperate fish detection and classification: a deep learning based approach,” Applied Intelligence, vol. 52, no. 6, pp. 6988–7001, Mar. 2021. https://doi.org/10.1007/s10489-020-02154-9 Search in Google Scholar

[17] M. Jahanbakht, W. Xiang, N. J. Waltham, and M. R. Azghadi, “Distributed deep learning in the cloud and energy-efficient real-time image processing at the edge for fish segmentation in underwater videos,” IEEE Access, vol. 10, pp. 117796–117807, Aug. 2022. https://doi.org/10.1109/ACCESS.2022.3202975 Search in Google Scholar

[18] S. Saputra, A. Yudhana, and R. Umar, “Implementation of naïve Bayes for fish freshness identification based on image processing,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 3, pp. 412–420, Jun. 2022. https://doi.org/10.29207/resti.v6i3.4062 Search in Google Scholar

[19] A. M. A. Aziz, I. Ahmad, S. M. M. Maharum, and Z. Mansor, “The development of a fish counting monitoring system using image processing,” in Advanced Materials and Engineering Technologies. Advanced Structured Materials, A. Ismail, W.M. Dahalan, and A. Öchsner, Eds. Springer, Cham, 2022, pp.255–262. https://doi.org/10.1007/978-3-030-92964-0_25 Search in Google Scholar

[20] L. T. M. D. Braga, A. Giraldo, and A. L. Godinho, “Evaluation of three methods for manually counting fish in dam turbines using DIDSON,” Hydrobiologia, vol. 849, pp. 309–321, May 2021. https://doi.org/10.1007/s10750-021-04605-x Search in Google Scholar

[21] C. S. Costa, V. A. G. Zanoni, L. R. V. Curvo, M. de Arau´jo Carvalho, W. R. Boscolo, A. Signor, M. dos Santos de Arruda, H. H. P. Nucci, J. M. Junior, W. N. Gonc¸alves, O. Diemer, and H. Pistori, “Deep learning applied in fish reproduction for counting larvae in images captured by smartphone,” Aquacultural Engineering, vol. 97, Art. no. 102225, May 2022. https://doi.org/10.1016/j.aquaeng.2022.102225 Search in Google Scholar

[22] Y. Wageeh, H. E.-D. Mohamed, A. Fadl, O. Anas, N. ElMasry, A. Nabil, and A. Atia, “YOLO fish detection with euclidean tracking in fish farms,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 1, pp. 5–12, Jan. 2021. https://doi.org/10.1007/s12652-020-02847-6 Search in Google Scholar

[23] J.-T. Yu, R.-S. Jia, Y.-C. Li, and H.-M. Sun, “Automatic fish counting via a multi-scale dense residual network,” Multimedia Tools and Applications, vol. 81, pp. 17223–17243, Mar. 2022. https://doi.org/10.1007/s11042-022-12672-y Search in Google Scholar

[24] J. Wu, Y. Zhou, H. Yu, Y. Zhang, and J. Li, “A novel fish counting method with adaptive weighted multi-dilated convolutional neural net-work,” in 2021 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS), London, United Kingdom, Dec. 2021, pp. 178–183. https://doi.org/10.1109/IUCC-CIT-DSCI-SmartCNS55181.2021.00039 Search in Google Scholar

[25] T. H. Khai, S. N. H. S. Abdullah, M. K. Hasan, and A. Tarmizi, “Underwater fish detection and counting using mask regional convolutional neural network,” Water, vol. 14, no. 2, Art. no. 222, Jan. 2022. https://doi.org/10.3390/w14020222 Search in Google Scholar

[26] N. Garcia-d’Urso, A. Galan-Cuenca, P. Pérez-Sánchez, P. Climent-Pérez, A. Fuster-Guillo, J. Azorin-Lopez, M. Saval-Calvo, J. E. Guillén-Nieto, and G. Soler-Capdepón, “The DeepFish computer vision dataset for fish instance segmentation, classification, and size estimation,” Scientific Data, vol. 9, Art. no. 287, Jun. 2022. https://doi.org/10.1038/s41597-022-01416-0 Search in Google Scholar

[27] P. Schober, M. H. Najafi, and N. Taherinejad, “High-accuracy multiply-accumulate (MAC) technique for unary stochastic computing,” IEEE Transactions on Computers, vol. 71, no. 6, pp. 1425–1439, Jun. 2022. https://doi.org/10.1109/tc.2021.3087027 Search in Google Scholar

[28] MathWorks team, “Bioinformatics Toolbox: User’s Guide (R2022a): Types of Morphological Operations - MATLAB & Simulink.” MathWorks [Online]. Available: https://www.mathworks.com/help/images/morphological-dilationand-erosion.html Search in Google Scholar

[29] E. R. Dougherty, An Introduction to Morphological Image Processing. Bellingham, WA: SPIE Press, Jan. 1992. Search in Google Scholar

[30] J. J. Serra, Image Analysis and Mathematical Morphology. San Diego, CA: Academic Press, 1983. Search in Google Scholar

[31] W.-J. Kim, S.-D. Kim, and K. Kim, “Fast algorithms for binary dilation and erosion using run-length encoding,” ETRI Journal, vol. 27, no. 6, pp. 814–817, Dec. 2005. https://doi.org/10.4218/etrij.05.0205.0013 Search in Google Scholar

[32] K. Valladares-Yánez, A. Monroy-Meza, R. Suárez-Rivera, J. Rodriguez-Reséndiz, G. Pérez-Soto, and K. Camarillo-Gómez, “Development and implementation of a vision system for decision making in the movements control of humanoid robots,” in 2018 XX Congreso Mexicano de Robótica (COMRob), Ensenada, Mexico, Sep 2018, pp. 1–6. https://doi.org/10.1109/comrob.2018.8689417 Search in Google Scholar

[33] M. Kahra, V. Sridhar, and M. Breuß, “Fast morphological dilation and erosion for grey scale images using the Fourier transform,” in Scale Space and Variational Methods in Computer Vision. SSVM 2021. Lecture Notes in Computer Science, A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, and L. Simon, Eds., vol 12679. Springer, Cham. Apr. 2021, pp. 65–77. https://doi.org/10.1007/978-3-030-75549-2_6 Search in Google Scholar

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