文档介绍：: .
河南工业大学。结果表明：将迁移学****卷积神经网络（CNN）和 DBN 结合的方法用于小麦不完善粒
识别，迁移学**** VGG-16+VGG-19+ResNet50-DBN 模型的识别效果最好，其准确率可达 %；所提出
的方法既避免了复杂的特征提取步骤，又使小麦不完善粒识别因数据集规模小而导致识别率不理想的问题
得到了改善；特征融合的方法使提取到的小麦图像信息更加丰富、全面。CNN-DBN 模型结合了有监督网
络和无监督网络的优点，对高维数据有更好的分类能力，为小麦不完善粒识别提供了理论支持。
关键词：迁移学****特征融合；卷积神经网络；深度信念网络；小麦不完善粒识别
文献标志码：A
DOI：
Research on unsound wheat kernels recognition technology based on CNN-DBN
Zhang Qinghui1, Tian Xinxin1, Lü Pengtao1, Yang Bin2
of Information Science and Engineering，Henan University of Technology，Zhengzhou 450001，China
Institute of Science and Technology Information, Zhengzhou 450000，China
Abstract: The recognition of unsound wheat kernels is an important part of wheat quality inspection, and it is also
a key indicator to measure wheat quality. Research on the recognition of unsound wheat kernels is of great
significance to the correct evaluation of wheat quality. In recent years, researches on the recognition of unsound
wheat kernels were mainly to directly optimize classical classification networks, and the problem of unsatisfactory
收稿日期：2021-12-01
基金项目：教育部粮食信息处理与控制重点实验室开放项目（KFJJ-2020-109）
作者简介：张庆辉（1974—），男，河南南阳人，教授，研究方向为智能信息处理，E-mail: ******@。
1recognition effect was often caused by insufficient wheat training dataset. Aiming at the problem of poor recognition
rate due to insufficient recognition data of unsound wheat kernels in practical application scenarios, we designed
and implemente