基于多支路卷积神经网络的磁瓦表面缺陷检测算法

doi:10.13229/j.cnki.jdxbgxb.20210855

Abstract

Abstract:

Aiming at the problem of great difficulty and low accuracy in the detection of magnetic tile surface defects（DMTSD）， a novel algorithm of DMTSD is proposed. In this paper， a multi-branch network structure is designed， and a convolutional neural network which can effectively extract the features of the magnetic tile images is constructed in each branch， and then the attention module is introduced to highlight the important features of the image. Finally， the correlation of the intra-class features and the difference of the inter-class features is maximized through discriminant correlation analysis， and the optimized fusion feature of magnetic tile images is obtained by concatenation fusion. The performance of the algorithm is tested on the magnetic tile image data set， and the test accuracy is up to 99.90%. In the actual detection work， the detection accuracy of the algorithm remains above 99%， and the detection speed reaches 129 pcs/min. The results of test and operation show that the algorithm has the advantage of high detection accuracy and stable performance， and can meet the requirements of real-time online detection in mass production of magnetic tiles.

Key words: computer application, magnetic tile, defect detection, multi-branch, convolutional neural network, feature fusion

CLC Number:

TP391

Pei-yong LIU,Jie DONG,Luo-feng XIE,Yang-yang ZHU,Guo-fu YIN. Surface defect detection algorithm of magnetic tiles based on multi⁃branch convolutional neural network[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1449-1457.

Figures/Tables 15

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Table 1

Performance test of four classical CNN in surface defect detection of magnetic tiles"

网络名称	训练精度	测试精度	平均测试时间 $t 1$ /s	单张图像平均测试时间 $t 2$ /s	最大测试时间 $t 3$ /s	最大平均测试时间 $t 4$ /s	检测速度n/（块·min^-1）
VGG16	0.9912	0.992	2.93	0.0293	3.45	0.0345	217
AlexNet	0.9842	0.984	1.99	0.0199	2.58	0.0258	290
ResNet18	0.9892	0.989	2.34	0.0234	2.39	0.0239	313
GoogLeNet	0.9848	0.985	4.69	0.0469	4.78	0.0478	156

Table 1

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References 13

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数据集	线状缺陷	块状缺陷	正常图像	合计
训练集	960	960	960	2880
验证集	320	320	320	960
测试集	320	320	320	960

实际类别	检测结果			样品数量
实际类别	合格	线状缺陷	块状缺陷	样品数量
合格	289/299	7/0	4/1	300
线状缺陷	16/3	279/296	5/1	300
块状缺陷	9/1	7/0	284/299	300
正确拒绝率	3.67% / 0.33%
错误接受率	6.17% / 0.83%
分类准确率	94.67% / 99.33%

支路网络	训练精度	测试精度
AlexNet	0.9929	0.9927
ResNet18	0.9959	0.9956
GoogLeNet	0.9937	0.9938
VGG16	0.9963	0.9958
IVGG	0.9987	0.9990

Surface defect detection algorithm of magnetic tiles based on multi⁃branch convolutional neural network

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