Fruit and Vegetables Classification System Using Image Saliency and Convolutional Neural Network

摘要：

　　Fruit and vegetables classification and recognition are still challenging in daily production and life.In this paper,we propose an efficient fruit and vegetables classification system using image saliency to draw the object regions and convolutional neural network(CNN)model to extract image features and implement classification.Image saliency is utilized to select main saliency regions according to saliency map.A VGG model is chosen to train for fruit and vegetables classification.Another contribution in this paper is that we establish a fruit and vegetables images database spanning 26 categories,which covers the major types in real life.Experiments are conducted on our own database,and the results show that our classification system achieves an excellent accuracy rate of 95.6%.

关键词： fruit and vegetables classification image saliency convolution neural network (CNN) VGG

作者: Guoxiang Zeng

作者单位: College of Information Engineering,Communication University of China Beijing,China

会议类型: 国际会议

会议名称: 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference(ITOEC2017)(2017 IEEE 第3届信息技术与机电一体化工程国际学术会议)

会议地点: 重庆

会议语种:英文

页码: 613-617

在线出版日期: 2017-10-03（万方平台首次上网日期，不代表论文的发表时间）