Deep learning applied in fish reproduction for counting larvae in images captured by smartphone

Aquacultural Engineering - Tập 97 - Trang 102225 - 2022
Celso Soares Costa1,2, Vanda Alice Garcia Zanoni3, Lucimar Rodrigues Vieira Curvo2,4, Mário de Araújo Carvalho5, Wilson Rogério Boscolo6, Altevir Signor6, Mauro dos Santos de Arruda5, Higor Henrique Picoli Nucci5, José Marcato Junior5, Wesley Nunes Gonçalves5, Odair Diemer7, Hemerson Pistori5,2
1Federal Institute of Education, Science and Technology of Mato Grosso do Sul, Ponta Porã/MS, Mato Grosso do Sul, Brazil
2Dom Bosco Catholic University, Campo Grande, Mato Grosso do Sul, Brazil
3University of Brasília, Brasília, Federal District, Brazil
4Federal Institute of Education, Science and Technology of Mato Grosso, Cuiabá, Mato Grosso, Brazil
5Federal University of Mato Grosso of Sul, Campo Grande, Mato Grosso of Sul, Brazil
6State University of West Paraná, Toledo, Paraná, Brazil
7Federal Institute of Education, Science and Technology of Mato Grosso do Sul, Coxim/MS, Mato Grosso do Sul, Brazil

Tài liệu tham khảo

Biffi, 2021, Atss deep learning-based approach to detect apple fruits, Remote Sens., 13 Boranga, 2018, Brazilian journal of development BJD, Braz. J. Dev., 5, 342 Cai, 2019, Cascade R-CNN: high quality object detection and instance segmentation, IEEE Trans. Pattern Anal. Mach. Intell., (pp. 1-1) Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., Zhang, Z., Cheng, D., Zhu, C., Cheng, T., Zhao, Q., Li, B., Lu, X., Zhu, R., Wu, Y., Dai, J., Wang, J., Shi, J., Ouyang, W., Loy, C. C., & Lin, D , 2019. Mmdetection: Open mmlab detection toolbox and benchmark.CoRR, abs/1906.07155. arXiv:1906.07155. Costa, 2019, A computer vision system for oocyte counting using images captured by smartphone, Aquac. Eng., 87, 10.1016/j.aquaeng.2019.102017 De Oliveira Filho, 2012, Evaluation of physicochemical and sensory properties of sausages made with washed and unwashed mince from nile tilapia by-products, J. Aquat. Food Prod. Technol., 21, 222, 10.1080/10498850.2011.590270 FAO , 2018. The State of World Fisheries and Aquaculture - Meeting the sustainable development goals. Food & Agriculture Org. http://www.fao.org/3/i9540en/I9540EN.pdf. Ferreira, 2019, QUANTIFICAÇÃO DE PÓS-LARVAS DE Macrobrachium rosenbergii MÉTODO VISUAL, VOLUMÉTRICO E FOTOGRÁFICO, XIII Reun. Científica do Inst. De. Pesca (13a ReCIP. ), 24 Goldman, E., Herzig, R., Eisenschtat, A., Goldberger, J., & Hassner, T., 2019. Precise detection in densely packed scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Lago, 2016, Residuos de Tilapia como materia prima para producción de salchichas: rendimiento y costo, La Rev. De. Cienc. Y. Tecnol. (RECyT), 18, 34 Lin, 2020, Focal loss for dense object detection, IEEE Trans. Pattern Anal. Mach. Intell., 42, 318, 10.1109/TPAMI.2018.2858826 Lu, X., Li, B., Yue, Y., Li, Q., & Yan, J., 2019a. Grid R-CNN. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 7355–7364. 10.1109/CVPR.2019.00754. arXiv:1811.12030. Lu, X., Li, B., Yue, Y., Li, Q., & Yan, J., 2019b. Grid r-cnn. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7363–7372. Navarro, 2010, Nutrição e alimentação de reprodutores de peixes, Temas Livres, 108 O’Grady, 2019, Edge computing: a tractable model for smart agriculture?, Artif. Intell. Agric., 3, 42 Osco, 2021, A cnn approach to simultaneously count plants and detect plantation-rows from uav imagery, ISPRS J. Photogramm. Remote Sens., 174, 1, 10.1016/j.isprsjprs.2021.01.024 Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., & Lin, D. (2019). Libra R-CNN: Towards balanced learning for object detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, pp. 821–830. 10.1109/CVPR.2019.00091. arXiv:1904.02701. Passarelli, 2018, Conectividade contínua e acesso móvel à informação digital: jovens brasileiros em perspectiva, Inf. Soc.: Estud., 28 Pimentel, 2014, Defective skeletogenesis and oversized otoliths in fish early stages in a changing ocean, J. Exp. Biol., 217, 2062 Raman, 2016, Computer assisted counter system for larvae and juvenile fish in Malaysian fishing hatcheries by machine learning approach, J. Comput., 11, 423, 10.17706/jcp.11.5.423-431 Ren, 2017, Faster R-CNN: towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 39, 1137, 10.1109/TPAMI.2016.2577031 Russakovsky, 2015, Imagenet large scale visual recognition challenge, Int. J. Comput. Vis., 115, 211, 10.1007/s11263-015-0816-y Santos, 2020, Storm-drain and manhole detection using the retinanet method, Sensors, 20, 4450, 10.3390/s20164450 Sun, K., Xiao, B., Liu, D., & Wang, J., 2019a. Deep high-resolution representation learning for human pose estimation.Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, pp. 5686–5696.10.1109/CVPR.2019.00584. arXiv:1902.09212. Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., & Wang, J., 2019b. High-resolution representations for labeling pixels and regions.CoRR, abs/1904.04514.arXiv:1904.04514. Tian, Z., Shen, C., Chen, H., & He, T., 2019. FCOS: Fully convolutional one-stage object detection.In: Proceedings of the IEEE International Conference on Computer Vision, 2019-October, pp. 9626–9635.10.1109/ICCV.2019.00972.arXiv:1904.01355. Zhang, S., Chi, C., Yao, Y., Lei, Z., & Li, S. Z., 2019. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. CoRR, abs/1912.02424.arXiv:1912.02424. Zhao, 2016, Spatial behavioral characteristics and statistics-based kinetic energy modeling in special behaviors detection of a shoal of fish in a recirculating aquaculture system, Comput. Electron. Agric., 127, 271, 10.1016/j.compag.2016.06.025 Zhu, C., He, Y., Savvides, M., 2019. Feature selective anchor-free module for single-shot object detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, pp. 840–849.10.1109/CVPR.2019.00093.arXiv:arXiv:1903.00621v1.