A hybrid deep learning-based fruit classification using attention model and convolution autoencoder
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ORIGINAL ARTICLE
A hybrid deep learning-based fruit classification using attention model and convolution autoencoder Gang Xue1 · Shifeng Liu1 · Yicao Ma1 Received: 1 July 2020 / Accepted: 18 August 2020 © The Author(s) 2020
Abstract 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. Keywords Fruit classification · DenseNet · CBAM · Convolutional neural networks
Introduction Nowadays, image classification method is very popular in a lot of fields, playing a pretty important role. Image recognition supports several applications, for instance, facial recognition, image classification, video analysis, and so on. Deep learning technology has been the core topic in machine learning and it has outstanding results in image identification [1]. Deep learning uses multilayer structure to process image features, which greatly enhance the performance of image identification [2]. Image recognition and deep learning are developing so fast and more and more fields benefit from them. Fresh supply chain, factories, supermarkets, etc. are the popular fields that are relying on image recognition and deep learning to obtain a better development. In other words, the application of image recognition and deep learning in logistics and supply chain field becomes a trend. For example, image recognition can help to guide the path of logistics and transportation, and it can solve the problem that several
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Gang Xue [email protected] School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
automatic transportation vehicles make mistakes because of large path identification errors [3]. Another example is fruit and vegetable classification. Deep learning can extract image features effectively and then implement classification. In the past, the fruit picking and processing is based on artificial methods, resulting in a large amount of waste of labor [4]. Recently,
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