TY - GEN
T1 - Tropical fruits classification using an alexnet-type convolutional neural network and image augmentation
AU - Patino-Saucedo, Alberto
AU - Rostro-Gonzalez, Horacio
AU - Conradt, Jorg
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - AlexNet CNN
KW - Convolutional neural networks
KW - Fruit classification
KW - Image augmentation
UR - http://www.scopus.com/inward/record.url?scp=85059035187&partnerID=8YFLogxK
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure_univeritat_ramon_llull&SrcAuth=WosAPI&KeyUT=WOS:000612948000032&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1007/978-3-030-04212-7_32
DO - 10.1007/978-3-030-04212-7_32
M3 - Conference contribution
AN - SCOPUS:85059035187
SN - 9783030042110
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 371
EP - 379
BT - Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
A2 - Ozawa, Seiichi
A2 - Leung, Andrew Chi Sing
A2 - Cheng, Long
PB - Springer Verlag
T2 - 25th International Conference on Neural Information Processing, ICONIP 2018
Y2 - 13 December 2018 through 16 December 2018
ER -