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Fruit Image Classification Based on MobileNetV2 with Transfer Learning Technique

Published: 22 October 2019 Publication History

Abstract

Fruit image classification is the key technology for robotic picking which can tremendously save costs and effectively improve fruit producer's competitiveness in the international fruit market. In the image classification field, deep learning technologies especially DCNNs are state-of-the-art technologies and have achieved remarkable success. But the requirements of high computation and storage resources prohibit the usages of DCNNs on resource-limited environments such as automatic harvesting robots. Therefore, we need to choose a lightweight neural network to achieve the balance of resource limitations and recognition accuracy. In this paper, a fruit image classification method based on a lightweight neural network MobileNetV2 with transfer learning technique was used to recognize fruit images. We used a MobileNetV2 network pre-trained by ImageNet dataset as a base network and then replace the top layer of the base network with a conventional convolution layer and a Softmax classifier. We applied dropout to the new-added conv2d at the same time to reduce overfitting. The pre-trained MobileNetV2 was used to extract features and the Softmax classifier was used to classify features. We trained this new model in two stages using Adam optimizer of different learning rate. This method finally achieved a classification accuracy of 85.12% in our fruit image dataset including 3670 images of 5 fruits. Compared with other network such as MobileNetV1, InceptionV3 and DenseNet121, this hybrid network implemented by Google open source deep learning framework Tensorflow can make a good compromise between accuracy and speed. Since MobileNetV2 is a lightweight neural network, the method in this paper can be deployed in low-power and limited-computing devices such as mobile phone.

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    cover image ACM Other conferences
    CSAE '19: Proceedings of the 3rd International Conference on Computer Science and Application Engineering
    October 2019
    942 pages
    ISBN:9781450362948
    DOI:10.1145/3331453
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 22 October 2019

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    Author Tags

    1. Depth separable convolutions
    2. Fruit image classification
    3. MobileNetV2
    4. Transfer learning

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    • National Natural Science Foundation of China

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    Overall Acceptance Rate 368 of 770 submissions, 48%

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