基于深度学习算法的带钢表面缺陷识别  

Strip steel surface defect recognition based on deep learning

作　　者：王立中[1] 管声启[1] WANG Lizhong;GUAN Shengqi(School of Mechanical and Electrical Engineering, Xi＇an Polytechnic University, Xi＇an 710048, China)

机构地区：[1]西安工程大学机电工程学院,陕西西安710048

出　　处：《西安工程大学学报》2017年第5期669-674,共6页Journal of Xi’an Polytechnic University

基　　金：西安市科技计划项目(2017074CG/RC037(XAGC006))

摘　　要：为了解决带钢表面缺陷识别过程中的特征不能自动准确提取的问题,给出了基于深度学习算法的带钢表面缺陷识别的新方法.本文在分析深度学习基本理论的基础上,建立了带钢表面识别的基础模型;然后,通过训练样本图像获取基础模型参数.该模型通过多隐层逐层抽取图像特征,从而自动获取目标的本质特征,进而进行识别分类;最后,通过实验验证本文算法的有效性.实验结果表明,本文带钢表面缺陷识别的准确率能达到98%以上,满足了带钢识别的要求.In order to solve the problem that the features in the process of strip surface defect i-dentification can not be automatically and accurately extracted.In this paper,a new approach to strip surface defect recognition based on deep learning is proposed.This paper first analyzes the basic theory of deep learning,on the basis of this,the basic model of strip surface recogni-tion is established.Then the basic model parameters are trained by the prepared training ima-ges.The model extracts image features layer by layer through hidden layers,thus automatically acquire the essential characteristics of the target.Identification and classification are performed. Finally,the validity of the model is verified by testing image.The experimental results show that the accuracy of strip surface defect identification can reach more than 98%,it meets the requirements of strip steel identification.

关 键 词：带钢表面 深度学习 分类准确性 缺陷识别 

分 类 号：TH83[机械工程—仪器科学与技术]