基于深度学习算法的带钢表面缺陷识别

摘要

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.%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.