计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 63-66.doi: 10.11896/jsjkx.200900163

• 图像处理&多媒体技术 • 上一篇    下一篇

一种基于图像分类的航空紧固件产品自动分类方法

胡京徽, 许鹏   

  1. 中国航空制造技术研究院 北京100024
  • 出版日期:2021-06-10 发布日期:2021-06-17
  • 通讯作者: 胡京徽(hujinghui@buaa.edu.cn)
  • 基金资助:
    国防基础科学研究计划(JCKY2018205B013)

Automatic Classification of Aviation Fastener Products Based on Image Classification

HU Jing-hui, XU Peng   

  1. AVIC Manufacturing Technology Institute,Beijing 100024,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:HU Jing-hui,born in 1992,master,assistant engineer.His main research interests include intelligent manufacturing technology,search-based software engineering and machine learning.
  • Supported by:
    National Defense Basic Scientific Research program of China(JCKY2018205B013).

摘要: 随着我国航空紧固件制造业的高速发展,生产车间流水线上的紧固件制造工艺变得越来越复杂。目前,生产流水线上工段内中不同规格产品的流转停留在人工分类阶段,这种做法不仅耗费人力,还很难满足实时处理分类需求。文中提出一种基于图像分类算法的航空紧固件自动分类方法,设计了一套紧固件图像采集和自动分类实施方案,并根据真实工业数据执行评估实验,评估实验统计了卷积神经网络和Inception-v3模型的准确率、查全率、查准率和F1值指标。实验结果表明,Inception-v3的各项评估指标优于卷积神经网络,Inception-v3模型分类的准确率达到98%以上,可以有效对航空紧固件产品实现自动分类。

关键词: Inception-v3, 航空紧固件, 卷积神经网络, 图像分类

Abstract: With the rapid development of aviation fastener manufacturing,the fastener manufacturing process on the production workshop assembly linebecomes more and more complicated.At present,the transfer of different fastener products in the production line still stays in the manual work.This method is not only complicated and tiring,but also difficult to satisfy the real-time classification requirement.In this paper,an automatic classification method for aviation fasteners based on image classification algorithms is proposed.A set of fastener image acquisition and automatic classification implementation schemes are designed,and evaluation experiments are performed based on real industrial data.The evaluation experiments count convolutional neural networks (CNN) and Inception-v3 model accuracy,recall,precision and F1-Score.The experimental results show that Inception-v3 is superior to CNN in various evaluation indicators,and the accuracy of Inception-v3 model classification reaches more than 98%,which can effectively realize automatic classification of aviation fastener products.

Key words: Aviation fastener, Convolutional neural network, Image classification, Inception-v3

中图分类号: 

  • TP301
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