A hybrid deep learning-based fruit classification using attention model and convolution autoencoder

Image recognition supports several applications, for instance, facial recognition, image classification, and achieving accurate fruit and vegetable classification is very important in fresh supply chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-base...

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Bibliographic Details
Published in:Complex & intelligent systems Vol. 9; no. 3; pp. 2209 - 2219
Main Authors: Xue, Gang, Liu, Shifeng, Ma, Yicao
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.06.2023
Springer Nature B.V
Springer
Subjects:
CBAM
Classification
Complexity
Computational Intelligence
Convolution
Convolutional neural networks
Data Structures and Information Theory
Deep learning
DenseNet
Effectiveness
Engineering
Face recognition
Factories
Fruit classification
Fruits
Image classification
Machine learning
Original Article
Supermarkets
Supply chains
DenseNet
Fruit classification
CBAM
Convolutional neural networks
ISSN:2199-4536, 2198-6053
Online Access:Get full text
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Summary:Image recognition supports several applications, for instance, facial recognition, image classification, and achieving accurate fruit and vegetable classification is very important in fresh supply chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN), which uses a convolution autoencoder to pre-train the images and uses an attention-based DenseNet to extract the features of image. In the first part of the framework, an unsupervised method with a set of images is applied to pre-train the greedy layer-wised CAE. We use CAE structure to initialize a set of weights and bias of ADN. In the second part of the framework, the supervised ADN with the ground truth is implemented. The final part of the framework makes a prediction of the category of fruits. We use two fruit datasets to test the effectiveness of the model, experimental results show the effectiveness of the framework, and the framework can improve the efficiency of fruit sorting, which can reduce costs of fresh supply chain, factories, supermarkets, etc.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-020-00192-x