基于深度学习的磁瓦表面缺陷分割与识别

谢舰; 姚剑敏; 严群; 林志贤

doi:10.37188/CJLCD.2020-0247

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基于深度学习的磁瓦表面缺陷分割与识别

图像处理 | 更新时间：2021-01-21

- 基于深度学习的磁瓦表面缺陷分割与识别
- Segmentation and recognition of magnetic tile surface defects based on deep learning
- 液晶与显示 2021年36卷第5期页码：713-722
- 作者机构：
  
  1.福州大学物理与信息工程学院, 福建福州 350108
  2.晋江市博感电子科技有限公司, 福建晋江 362200
- 作者简介：
  
  [ "谢舰(1996-), 男, 江西赣州人, 硕士研究生, 2018年于江西理工大学获得学士学位, 主要从事深度学习、图像处理方面的研究。E-mail:1653698129@qq.com" ]
  [ "姚剑敏(1978-), 男, 福建莆田人, 博士, 副研究员, 2005年于中国科学院长春光学精密机械与物理研究所获得博士学位, 主要从事人工智能、图像处理、信息显示技术等方面的研究。E-mail: yaojm@fzu.edu.cn" ]
- 基金信息：
  
  国家重点研发计划资助(2016YFB0401503);广东省科技重大专项(2016B090906001);福建省科技重大专项(2014HZ0003-1);Funded by The Guangdong Provincial Key Laboratory of Optical Information Materials and Technology(2017B030301007)
- DOI：10.37188/CJLCD.2020-0247
  中图分类号： TP394.1;TH691.9
- 收稿日期：2020-09-24，
  
  修回日期：2020-10-28，
  
  纸质出版日期：2021-05
- 稿件说明：
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谢舰, 姚剑敏, 严群, 等. 基于深度学习的磁瓦表面缺陷分割与识别[J]. 液晶与显示, 2021,36(5):713-722.

Jian XIE, Jian-min YAO, Qun YAN, et al. Segmentation and recognition of magnetic tile surface defects based on deep learning[J]. Chinese journal of liquid crystals and displays, 2021, 36(5): 713-722.
谢舰, 姚剑敏, 严群, 等. 基于深度学习的磁瓦表面缺陷分割与识别[J]. 液晶与显示, 2021,36(5):713-722. DOI： 10.37188/CJLCD.2020-0247.

Jian XIE, Jian-min YAO, Qun YAN, et al. Segmentation and recognition of magnetic tile surface defects based on deep learning[J]. Chinese journal of liquid crystals and displays, 2021, 36(5): 713-722. DOI： 10.37188/CJLCD.2020-0247.

摘要

为了满足磁瓦生产工业对表面质量检测的高要求，实现磁瓦缺陷自动分割与识别，本文提出了一种基于卷积神经网络的缺陷分割与分类网络。该网络基于U-net架构，通过U-net编码部分提取缺陷的深层特征，并使用该深层特征进行缺陷分类，然后通过解码部分输出分割的缺陷区域。为了解决部分缺陷前景面积占比太小，导致网络难以收敛的问题，通过添加差异系数损失以保证网络持续优化。然后在训练阶段添加多层损失和进行在线数据增强进一步提升了分割精度和分类准确率。实验结果表明，添加辅助损失函数和数据增强后，分割网络能够分割出94.5%标注的缺陷区域，并且对于缺陷分类的准确率能够达到98.9%，满足工业生产的高精度要求。该方法能够精准有效地分割和识别磁瓦的表面缺陷，为磁瓦表面质量检测自动化行业提供了一种新的思路。

Abstract

In order to meet the high requirements of the magnetic tile production industry for surface quality inspection and realize the automatic segmentation and recognition of magnetic tile defects

a defect segmentation and classification network based on convolutional neural networks is proposed. The network is based on the U-net architecture. The deep features of defects are extracted through the U-net encoding part

and the deep features are used for defect classification

and then the segmented defect areas are output through the decoding part. In order to solve the problem that the proportion of the foreground area of some defects is too small

which makes the network difficult to converge

the continuous optimization of the network is ensured by adding the difference coefficient loss. Then

adding multiple layers of loss and performing online data enhancement in the training phase further improves the segmentation accuracy and classification accuracy. Experimental results show that with the addition of auxiliary loss function and data enhancement

the segmentation network can segment 94.5% of the marked defect areas

and the accuracy of defect classification can reach 98.9%

which meets the high precision requirements of the industry. This method can accurately and effectively segment and identify the surface defects of the magnetic tile

which provides a new idea for the automatic industry of magnetic tile surface quality inspection.

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references

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