摘要
针对现今的智能垃圾桶使用方法大都无法应用在列车的问题,提出了一种基于迁移学习方法的智能垃圾分类方法.首先建立了12种列车常见垃圾数据集,并应用迁移学习ResNet50模型进行模型设计与参数设计,给出了模型的准确率与模型收敛速度曲线,并与Inception-V3模型进行了对比实验,实验结果表明ResNet50模型有更好的识别效果.最后通过PyQt5编写GUI仿真系统,实现了基本的拍照与分类识别功能.
For the most of the current methods of using intelligent garbage which cannot be applied to the train,an intelligent garbage classification method is presented based on migration learning method.12 kinds of common garbage data sets of trains are established,and the model and parameter design are carried out by u-sing the resnet50 model of migration learning.The accuracy and convergence speed curve of the model are given,and the comparison experiment with the concept-v3 model is conducted.The experiment results show that the resnet50 model has better recognition effect.Finally,the GUI simulation system is programmed by pyqt5 to realize the basic functions of photographing and classification.
作者
张丽艳
赵艺璇
牟钰鹏
李林
ZHANG Liyan;ZHAO Yixuan;MU Yupeng;LI Lin(School of Electrical&Information Engineering,Dalian Jiaotong University,Dalian 116028,China)
出处
《大连交通大学学报》
CAS
2021年第4期101-105,共5页
Journal of Dalian Jiaotong University
基金
辽宁省教育厅科学研究计划资助项目(JDL2020029)。
