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Tropical fruits classification using an alexnet-type convolutional neural network and image augmentation
KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Technical University of Munich, Germany.
2018 (English)In: 25th International Conference on Neural Information Processing, ICONIP 2018, Springer, 2018, Vol. 11304, p. 371-379Conference paper, Published paper (Refereed)
Abstract [en]

AlexNet is a Convolutional Neural Network (CNN) and reference in the field of Machine Learning for Deep Learning. It has been successfully applied to image classification, especially in large sets such as ImageNet. Here, we have successfully applied a smaller version of the AlexNet CNN to classify tropical fruits from the Supermarket Produce dataset. This database contains 2633 images of fruits divided into 15 categories with high variability and complexity, i.e. shadows, pose, occlusion, reflection (fruits inside a bag), etc. Since few training samples are required for fruit classification and to prevent overfitting, the modified AlexNet CNN has fewer feature maps and fully connected neurons than the original one, and data augmentation of the training set is used. Numerical results show a top-1 classification accuracy of 99.56 %, and a top-2 accuracy of 100 % for the 15 classes, which outperforms previous works on the same dataset.

Place, publisher, year, edition, pages
Springer, 2018. Vol. 11304, p. 371-379
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 11304
Keywords [en]
AlexNet CNN, Convolutional neural networks, Fruit classification, Image augmentation
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-241478DOI: 10.1007/978-3-030-04212-7_32Scopus ID: 2-s2.0-85059035187ISBN: 9783030042110 (print)OAI: oai:DiVA.org:kth-241478DiVA, id: diva2:1281701
Conference
25th International Conference on Neural Information Processing, ICONIP 2018, Siem Reap, Cambodia, 13 December 2018 through 16 December 2018
Note

QC 20190123

Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-01-23Bibliographically approved

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Conradt, Jörg

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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