基于深度学习和迁移学习的水果图像分类

Fruit Image Classification Based on Deep Learning and Transfer Learning

中文关键词:  图像识别  深度学习  Softmax  迁移学习

英文关键词:image classification  deep learning  Softmax  transfer learning

中文摘要:

      图像识别作为深度学习领域内的一项重要应用，水果图像的分类识别在智慧农业以及采摘机器人等方面具有重要应用。针对以往传统图像分类算法存在泛化能力差、准确率不高等问题，提出一种在TensorFlow框架下基于深度学习和迁移学习的水果图像分类算法。该算法采用Inception-V3的部分模型结构对水果图像数据进行特征提取，采用Softmax分类器对图像特征进行分类，并通过迁移学习方式进行训练得到迁移训练模型。测试结果表明，该算法与传统水果分类算法对比，具有较高识别准确率。

英文摘要:

      Image recognition is an important application in the field of deep learning.The classification and recognition of fruit images has important applications in smart agriculture and picking robots.Inorder to solve the problems of poor generalization ability and low accuracy in traditional image classification algorithms,a fruit image classification algorithm based on deep learning and migration learning under TensorFlow framework is proposed.The partial model structure of Inception-V3 is used to extract the feature image of fruit image,and Softmax classifier is used to classify the image features,which trained by migration learning to obtain the migration training model.The test results show that the algorithm has higher recognition accuracy than the traditional fruit classification algorithm.