An automated classification for danger of extinction Animals from Colombia using convolutional neural networks
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animals, extinction, artificial neural networks
The extinction of different types of animals is a problem that has been growing over the years, and that, consequently, has caused environmental problems, such as climate change. Genetic diversity (biodiversity) is essential for the development of all species and human beings depend on it in their daily lives. When biodiversity decreases, human life expectancy is reduced, not only from an ecological point of view, but also from a resource point of view, even to be able to have species that are adapted to an ecological niche. This research will expose a computer strategy that over time has achieved great results; convolutional neural networks is a process that has facilitated the monitoring of different kinds of animals in recent years, this, in order to facilitate the process of recognition and counting of animals, focused on agriculture and zoology. For this, an architecture in the field of convolutional neural networks (CNN) will be used, Alexnet, which has references with very high results. In addition, the mathematical programming software Matlab is used for the development of the neural network. Getting of this way a result of accuracy of validation of 97,52%, with the use of a dataset with 3026 images, in where, 80% are used for training and 20% for validation.
“Que son las redes neuronales y sus funciones,” 22 Oct 2019 . [En línea]. Disponible en: https://www.atriainnovation.com/que-son-las-redes-neuronales-y-sus funciones/#:~:text=Las%20redes%20neuronales%20artificiales%20son,entrada%20hasta%20generar%20una%20salia
S. Silva and E. Freire “Intro a las redes neuronales convolucionales”, 23 Nov 2019. [En línea]. Disponible en: https://bootcampai.medium.com/redes-neuronales-convolucionales-5e0ce960caf8
G. Li, L. Bai, C. Zhu, E. Wu and R. Ma, "A Novel Method of Synthetic CT Generation from MR Images Based on Convolutional Neural Networks", 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1-5, 2018. doi: 10.1109/CISP-BMEI.2018.8633142 DOI: https://doi.org/10.1109/CISP-BMEI.2018.8633142
K. Singh, A. Seth, H. S. Sandhu and K. Samdani, "A Comprehensive Review of Convolutional Neural Network based Image Enhancement Techniques", IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1-6, 2019. doi: 10.1109/ICSCAN.2019.8878706 DOI: https://doi.org/10.1109/ICSCAN.2019.8878706
B. Chen, J. Li, B. Ma and G. Wei, "Convolutional sparse coding classification model for image classification", IEEE International Conference on Image Processing (ICIP), pp. 1918-1922, 2016. doi: 10.1109/ICIP.2016.7532692 DOI: https://doi.org/10.1109/ICIP.2016.7532692
Z. Gong, C. Sun, W. Guo, W. Tan, W. Zhou and G. Zhang, "Automated Thalamus Segmentation in MR Images Using Convolutional Networks", IEEE 3rd International Conference of Safe Production and Informatization (IICSPI), pp. 158-161, 2020. doi: 10.1109/IICSPI51290.2020.9332452 DOI: https://doi.org/10.1109/IICSPI51290.2020.9332452
C. Jia, X. Zhang, J. Zhang, S. Wang and S. Ma, "Deep convolutional network based image quality enhancement for low bit rato image compression", Visual Communications and Image Processing (VCIP), pp. 1-4, 2016. doi: 10.1109/VCIP.2016.7805504 DOI: https://doi.org/10.1109/VCIP.2016.7805504
N. Manjón “16 animales en peligro de extinción en Colombia”, 10 Agosto 2020. [En línea]. Disponible en: https://www.ecologiaverde.com/16-animales-en-peligro-de-extincion-en-colombia-1909.html
A. Noor, Y. Q. Zhao, A. Koubaa, L. W. Wu, R. Khan and F. Y. O. Abdalla, “Automated sheep facial expression classification using deep transfer learning”, COMPUTERS AND ELECTRONICS IN AGRICULTURE. [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do product=WOS&search_cheurlFromRightClick=no
F. de Lima, de Moraes V. Aparecida, Menezes, G. Vilharva, “Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks”, COMPUTERS AND ELECTRONICS IN AGRICULTURE. [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.oc=4&cacheurlFromRightClick=no
M. Mathieu, M. Jiangqiang, S. Xiaocai, “An adaptive pig face recognition approach using Convolutional Neural Networks” COMPUTERS AND ELECTRONICS IN AGRICULTURE [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do?product=WOS&search_oc=11&cacheurlFromRightClick=no
C. Daegyu, C. Eunjeong, K. Dong Keun, “The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning” MOBILE INFORMATION SYSTEMS. [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do?product=WOS&search_c=24&cacheurlFromRightClick=no
Y. Dmitry; S. Anton; K. Andrey, “Detection of Big Animals on Images with Road Scenes using Deep Learning” Conferencia: International Conference on Artificial Intelligence - Applications and Innovations (IC-AIAI). [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do?product=WOS&search_oc=76&cacheurlFromRightClick=no
B. Arnginn, P. Thongkanchorn, K. Kanchanapreechakorn, “Breakthrough Conventional Based Approach for Dog Breed Classification Using CNN with Transfer Learning”, Conferencia: 11th International Conference on Information Technology and Electrical Engineering (ICITEE). [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do product=WOS&search_oc=79&cacheurlFromRightClick=no
X. Liu, Z. Jia, X. Hou, M. Fu, L. Ma, Q. Sun, “Real-time Marine Animal Images Classification by Embedded System Based on Mobilenet and Transfer Learning”, OCEANS. 2019. [Online]. Available: https://ieeexplore-ieee-org.bdbiblioteca.ufps.edu.co/document/8867190/ DOI: https://doi.org/10.1109/OCEANSE.2019.8867190
N. K. El Abbadi, E. Mohammed Thabit A. Alsaadi, “An Automated Vertebrate Animals Classification Using Deep Convolution Neural Networks”, International Conference on Computer Science and Software Engineering (CSASE), 2020. [Online]. Available: https://ieeexplore-ieee-org.bdbiblioteca.ufps.edu.co/document/9142070/ DOI: https://doi.org/10.1109/CSASE48920.2020.9142070
S. Jamil, Fawad, M. S. Abbas, F. Habib, M. Umair, M. J. Khan, “Deep Learning and Computer Vision-based a Novel Framework for Himalayan Bear, Marco Polo Sheep and Snow Leopard Detection”, International Conference on Information Science and Communication Technology (ICISCT). 2020. [Online]. Available: https://ieeexplore-ieee-org.bdbiblioteca.ufps.edu.co/document/9080021/ DOI: https://doi.org/10.1109/ICISCT49550.2020.9080021
X. Huang, X. Li, Z. Hu, “Cow tail detection method for body condition score using Faster R-CNN”, IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI), 2019. [Online]. Available: https://ieeexplore-ieee-org.bdbiblioteca.ufps.edu.co/document/9124743/ DOI: https://doi.org/10.1109/ICUSAI47366.2019.9124743
J. Wei, “AlexNet: The Architecture that Challenged CNNs”, 2019. [Online]. Available: https://towardsdatascience.com/alexnet-the-architecture-that-challenged-cnns-e406d5297951
D. Jain y A. Dong, “Funcionamiento de una red neuronal convolucional (CNN) y la arquitectura AlexNet”, 2020. [En línea]. Disponible en: https://ichi.pro/es/funcionamiento-de-una-red-neuronal-convolucional-cnn-y-la-arquitectura-alexnet-233779119045871
A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, 2012. [Online]. Available: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
The extinction of different types of animals is a problem that has been growing over the years, and that, consequently, has caused environmental problems, such as climate change. Genetic diversity (biodiversity) is essential for the development of all species and human beings depend on it in their daily lives. When biodiversity decreases, human life expectancy is reduced, not only from an ecological point of view, but also from a resource point of view, even to be able to have species that are adapted to an ecological niche. This research will expose a computer strategy that over time has achieved great results; convolutional neural networks is a process that has facilitated the monitoring of different kinds of animals in recent years, this, in order to facilitate the process of recognition and counting of animals, focused on agriculture and zoology. For this, an architecture in the field of convolutional neural networks (CNN) will be used, Alexnet, which has references with very high results. In addition, the mathematical programming software Matlab is used for the development of the neural network. Getting of this way a result of accuracy of validation of 97,52%, with the use of a dataset with 3026 images, in where, 80% are used for training and 20% for validation.
“Que son las redes neuronales y sus funciones,” 22 Oct 2019 . [En línea]. Disponible en: https://www.atriainnovation.com/que-son-las-redes-neuronales-y-sus funciones/#:~:text=Las%20redes%20neuronales%20artificiales%20son,entrada%20hasta%20generar%20una%20salia
S. Silva and E. Freire “Intro a las redes neuronales convolucionales”, 23 Nov 2019. [En línea]. Disponible en: https://bootcampai.medium.com/redes-neuronales-convolucionales-5e0ce960caf8
G. Li, L. Bai, C. Zhu, E. Wu and R. Ma, "A Novel Method of Synthetic CT Generation from MR Images Based on Convolutional Neural Networks", 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1-5, 2018. doi: 10.1109/CISP-BMEI.2018.8633142 DOI: https://doi.org/10.1109/CISP-BMEI.2018.8633142
K. Singh, A. Seth, H. S. Sandhu and K. Samdani, "A Comprehensive Review of Convolutional Neural Network based Image Enhancement Techniques", IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1-6, 2019. doi: 10.1109/ICSCAN.2019.8878706 DOI: https://doi.org/10.1109/ICSCAN.2019.8878706
B. Chen, J. Li, B. Ma and G. Wei, "Convolutional sparse coding classification model for image classification", IEEE International Conference on Image Processing (ICIP), pp. 1918-1922, 2016. doi: 10.1109/ICIP.2016.7532692 DOI: https://doi.org/10.1109/ICIP.2016.7532692
Z. Gong, C. Sun, W. Guo, W. Tan, W. Zhou and G. Zhang, "Automated Thalamus Segmentation in MR Images Using Convolutional Networks", IEEE 3rd International Conference of Safe Production and Informatization (IICSPI), pp. 158-161, 2020. doi: 10.1109/IICSPI51290.2020.9332452 DOI: https://doi.org/10.1109/IICSPI51290.2020.9332452
C. Jia, X. Zhang, J. Zhang, S. Wang and S. Ma, "Deep convolutional network based image quality enhancement for low bit rato image compression", Visual Communications and Image Processing (VCIP), pp. 1-4, 2016. doi: 10.1109/VCIP.2016.7805504 DOI: https://doi.org/10.1109/VCIP.2016.7805504
N. Manjón “16 animales en peligro de extinción en Colombia”, 10 Agosto 2020. [En línea]. Disponible en: https://www.ecologiaverde.com/16-animales-en-peligro-de-extincion-en-colombia-1909.html
A. Noor, Y. Q. Zhao, A. Koubaa, L. W. Wu, R. Khan and F. Y. O. Abdalla, “Automated sheep facial expression classification using deep transfer learning”, COMPUTERS AND ELECTRONICS IN AGRICULTURE. [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do product=WOS&search_cheurlFromRightClick=no
F. de Lima, de Moraes V. Aparecida, Menezes, G. Vilharva, “Recognition of Pantaneira cattle breed using computer vision and convolutional neural networks”, COMPUTERS AND ELECTRONICS IN AGRICULTURE. [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.oc=4&cacheurlFromRightClick=no
M. Mathieu, M. Jiangqiang, S. Xiaocai, “An adaptive pig face recognition approach using Convolutional Neural Networks” COMPUTERS AND ELECTRONICS IN AGRICULTURE [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do?product=WOS&search_oc=11&cacheurlFromRightClick=no
C. Daegyu, C. Eunjeong, K. Dong Keun, “The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning” MOBILE INFORMATION SYSTEMS. [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do?product=WOS&search_c=24&cacheurlFromRightClick=no
Y. Dmitry; S. Anton; K. Andrey, “Detection of Big Animals on Images with Road Scenes using Deep Learning” Conferencia: International Conference on Artificial Intelligence - Applications and Innovations (IC-AIAI). [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do?product=WOS&search_oc=76&cacheurlFromRightClick=no
B. Arnginn, P. Thongkanchorn, K. Kanchanapreechakorn, “Breakthrough Conventional Based Approach for Dog Breed Classification Using CNN with Transfer Learning”, Conferencia: 11th International Conference on Information Technology and Electrical Engineering (ICITEE). [Online]. Available: http://apps.webofknowledge.com.bdbiblioteca.ufps.edu.co:2048/full_record.do product=WOS&search_oc=79&cacheurlFromRightClick=no
X. Liu, Z. Jia, X. Hou, M. Fu, L. Ma, Q. Sun, “Real-time Marine Animal Images Classification by Embedded System Based on Mobilenet and Transfer Learning”, OCEANS. 2019. [Online]. Available: https://ieeexplore-ieee-org.bdbiblioteca.ufps.edu.co/document/8867190/ DOI: https://doi.org/10.1109/OCEANSE.2019.8867190
N. K. El Abbadi, E. Mohammed Thabit A. Alsaadi, “An Automated Vertebrate Animals Classification Using Deep Convolution Neural Networks”, International Conference on Computer Science and Software Engineering (CSASE), 2020. [Online]. Available: https://ieeexplore-ieee-org.bdbiblioteca.ufps.edu.co/document/9142070/ DOI: https://doi.org/10.1109/CSASE48920.2020.9142070
S. Jamil, Fawad, M. S. Abbas, F. Habib, M. Umair, M. J. Khan, “Deep Learning and Computer Vision-based a Novel Framework for Himalayan Bear, Marco Polo Sheep and Snow Leopard Detection”, International Conference on Information Science and Communication Technology (ICISCT). 2020. [Online]. Available: https://ieeexplore-ieee-org.bdbiblioteca.ufps.edu.co/document/9080021/ DOI: https://doi.org/10.1109/ICISCT49550.2020.9080021
X. Huang, X. Li, Z. Hu, “Cow tail detection method for body condition score using Faster R-CNN”, IEEE International Conference on Unmanned Systems and Artificial Intelligence (ICUSAI), 2019. [Online]. Available: https://ieeexplore-ieee-org.bdbiblioteca.ufps.edu.co/document/9124743/ DOI: https://doi.org/10.1109/ICUSAI47366.2019.9124743
J. Wei, “AlexNet: The Architecture that Challenged CNNs”, 2019. [Online]. Available: https://towardsdatascience.com/alexnet-the-architecture-that-challenged-cnns-e406d5297951
D. Jain y A. Dong, “Funcionamiento de una red neuronal convolucional (CNN) y la arquitectura AlexNet”, 2020. [En línea]. Disponible en: https://ichi.pro/es/funcionamiento-de-una-red-neuronal-convolucional-cnn-y-la-arquitectura-alexnet-233779119045871
A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks”, 2012. [Online]. Available: https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html