Fruit Image Classification Based on MobileNetV2 with Transfer Learning Technique

摘要

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.