Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning
Automatic image recognition is a convenient option for labeling and categorizing fruits and vegetables in supermarkets. This paper proposes the design and implementation of an automatic classification system for Colombian fruits, by training a convolutional neural network. A database was created to...
- Autores:
-
Alvarez-Canchila, O.I.
Arroyo-Pérez, D.E
Patiňo-Saucedo, A.
Rostro González, H.
Patĩo-Vanegas, A.
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12356
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12356
- Palabra clave:
- Object Detection;
Deep Learning;
IOU
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv |
Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning |
title |
Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning |
spellingShingle |
Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning Object Detection; Deep Learning; IOU LEMB |
title_short |
Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning |
title_full |
Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning |
title_fullStr |
Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning |
title_full_unstemmed |
Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning |
title_sort |
Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning |
dc.creator.fl_str_mv |
Alvarez-Canchila, O.I. Arroyo-Pérez, D.E Patiňo-Saucedo, A. Rostro González, H. Patĩo-Vanegas, A. |
dc.contributor.author.none.fl_str_mv |
Alvarez-Canchila, O.I. Arroyo-Pérez, D.E Patiňo-Saucedo, A. Rostro González, H. Patĩo-Vanegas, A. |
dc.subject.keywords.spa.fl_str_mv |
Object Detection; Deep Learning; IOU |
topic |
Object Detection; Deep Learning; IOU LEMB |
dc.subject.armarc.none.fl_str_mv |
LEMB |
description |
Automatic image recognition is a convenient option for labeling and categorizing fruits and vegetables in supermarkets. This paper proposes the design and implementation of an automatic classification system for Colombian fruits, by training a convolutional neural network. A database was created to train and test the system, which consisted of 4980 images, labeled in 22 classes, each corresponding to pictures of the same kind of fruit, trying to reproduce the variability of a real case scenario with occlusions, different positions, rotations, lightings, colors, etc., and the use of bags. On-training data augmentation was used to further increase the robustness of the model. Additionally, transfer learning was implemented by taking the parameters of a pretrained model used for fruit classification as the new initial parameters of the proposed convolutional network, achieving an increase of the classification accuracy compared with the same model when trained with random initial weights. The final classification accuracy of the network was 98.12% which matches the scores achieved on previous works that performed fruit classification on less challenging datasets. Considering top-3 classification we report an accuracy of 99.95%. © 2020 IOP Publishing Ltd. All rights reserved. |
publishDate |
2020 |
dc.date.issued.none.fl_str_mv |
2020 |
dc.date.accessioned.none.fl_str_mv |
2023-07-21T16:26:52Z |
dc.date.available.none.fl_str_mv |
2023-07-21T16:26:52Z |
dc.date.submitted.none.fl_str_mv |
2023 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/draft |
dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
status_str |
draft |
dc.identifier.citation.spa.fl_str_mv |
Álvarez-Canchila, O. I., Arroyo-Pérez, D. E., Patiňo-Saucedo, A., González, H. R., & Patino-Vanegas, A. (2020, May). Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning. In Journal of Physics: Conference Series (Vol. 1547, No. 1, p. 012020). IOP Publishing. |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12356 |
dc.identifier.doi.none.fl_str_mv |
10.1088/1742-6596/1547/1/012020 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
identifier_str_mv |
Álvarez-Canchila, O. I., Arroyo-Pérez, D. E., Patiňo-Saucedo, A., González, H. R., & Patino-Vanegas, A. (2020, May). Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning. In Journal of Physics: Conference Series (Vol. 1547, No. 1, p. 012020). IOP Publishing. 10.1088/1742-6596/1547/1/012020 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
url |
https://hdl.handle.net/20.500.12585/12356 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.cc.*.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.extent.none.fl_str_mv |
7 páginas |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
dc.source.spa.fl_str_mv |
Journal of Physics: Conference Series |
institution |
Universidad Tecnológica de Bolívar |
bitstream.url.fl_str_mv |
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Alvarez-Canchila, O.I.6e315db7-f4a7-4906-a037-3e16a9439cbaArroyo-Pérez, D.E5af0863a-702e-4e04-aa7e-38bd47293639Patiňo-Saucedo, A.b1408d99-dd09-4d80-8943-1ef586b6e06fRostro González, H.e0f28ea6-3fc7-4ad2-ade9-243a3fb477c4Patĩo-Vanegas, A.6b39300f-cb9a-4f5d-9e41-f47f5557214a2023-07-21T16:26:52Z2023-07-21T16:26:52Z20202023Álvarez-Canchila, O. I., Arroyo-Pérez, D. E., Patiňo-Saucedo, A., González, H. R., & Patino-Vanegas, A. (2020, May). Colombian fruit and vegetables recognition using convolutional neural networks and transfer learning. In Journal of Physics: Conference Series (Vol. 1547, No. 1, p. 012020). IOP Publishing.https://hdl.handle.net/20.500.12585/1235610.1088/1742-6596/1547/1/012020Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarAutomatic image recognition is a convenient option for labeling and categorizing fruits and vegetables in supermarkets. This paper proposes the design and implementation of an automatic classification system for Colombian fruits, by training a convolutional neural network. A database was created to train and test the system, which consisted of 4980 images, labeled in 22 classes, each corresponding to pictures of the same kind of fruit, trying to reproduce the variability of a real case scenario with occlusions, different positions, rotations, lightings, colors, etc., and the use of bags. On-training data augmentation was used to further increase the robustness of the model. Additionally, transfer learning was implemented by taking the parameters of a pretrained model used for fruit classification as the new initial parameters of the proposed convolutional network, achieving an increase of the classification accuracy compared with the same model when trained with random initial weights. The final classification accuracy of the network was 98.12% which matches the scores achieved on previous works that performed fruit classification on less challenging datasets. Considering top-3 classification we report an accuracy of 99.95%. © 2020 IOP Publishing Ltd. All rights reserved.7 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference SeriesColombian fruit and vegetables recognition using convolutional neural networks and transfer learninginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Object Detection;Deep Learning;IOULEMBCartagena de IndiasDubey, S.R., Jalal, A.S. Robust approach for fruit and vegetable classification (2012) Procedia Engineering, 38, pp. 3449-3453. Cited 41 times. http://www.sciencedirect.com/science/journal/18777058 doi: 10.1016/j.proeng.2012.06.398Rocha, A., Hauagge, D.C., Wainer, J., Goldenstein, S. Automatic fruit and vegetable classification from images (Open Access) (2010) Computers and Electronics in Agriculture, 70 (1), pp. 96-104. Cited 208 times. doi: 10.1016/j.compag.2009.09.002Zawbaa, H.M., Abbass, M., Hazman, M., Hassenian, A.E. Automatic fruit image recognition system based on shape and color features (2014) Communications in Computer and Information Science, 488, pp. 278-290. Cited 43 times. http://www.springer.com/series/7899 ISBN: 978-331913460-4 doi: 10.1007/978-3-319-13461-1_27Zhang, Y., Wang, S., Ji, G., Phillips, P. Fruit classification using computer vision and feedforward neural network (2014) Journal of Food Engineering, 143, pp. 167-177. Cited 235 times. http://www.sciencedirect.com/science/journal/02608774 doi: 10.1016/j.jfoodeng.2014.07.001LeCun, Y., Bottou, L., Bengio, Y., Haffner, P. Gradient-based learning applied to document recognition (1998) Proceedings of the IEEE, 86 (11), pp. 2278-2323. Cited 33025 times. doi: 10.1109/5.726791Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., (...), Chen, T. Recent advances in convolutional neural networks (Open Access) (2018) Pattern Recognition, 77, pp. 354-377. Cited 2608 times. www.elsevier.com/inca/publications/store/3/2/8/ doi: 10.1016/j.patcog.2017.10.013Patino-Saucedo, A., Rostro-Gonzalez, H., Conradt, J. Tropical fruits classification using an alexnet-type convolutional neural network and image augmentation (2018) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11304 LNCS, pp. 371-379. Cited 10 times. https://www.springer.com/series/558 ISBN: 978-303004211-0 doi: 10.1007/978-3-030-04212-7_32Krizhevsky, A., Sutskever, I., Hinton, G.E. ImageNet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, 2, pp. 1097-1105. Cited 71780 times. ISBN: 978-162748003-1Taylor, L., Nitschke, G. Improving Deep Learning with Generic Data Augmentation (2019) Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, art. no. 8628742, pp. 1542-1547. Cited 233 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8610062 ISBN: 978-153869276-9 doi: 10.1109/SSCI.2018.8628742Yosinski, J., Clune, J., Bengio, Y., Lipson, H. How transferable are features in deep neural networks? (2014) Advances in Neural Information Processing Systems, 4 (January), pp. 3320-3328. Cited 4861 times.Bisong, E. (2019) Google Colaboratory Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, pp. 59-64. Cited 798 times.Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., (...), Zheng, X. TensorFlow: A system for large-scale machine learning (Open Access) (2016) Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, pp. 265-283. Cited 12340 times. 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