Open AccessIntelligent recognition of surface defects of parts by Resnet
Author(s) -
Fushuai Wang,
Jiyuan Qiu,
Zhefeng Wang,
Wenrui Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012178
Subject(s) - convolution (computer science) , computer science , artificial neural network , surface (topology) , field (mathematics) , residual neural network , convergence (economics) , pattern recognition (psychology) , set (abstract data type) , convolutional neural network , artificial intelligence , identification (biology) , data mining , mathematics , geometry , botany , pure mathematics , economics , biology , programming language , economic growth
Nowadays, Automatic metal surface defect recognition is an important research direction in the field of surface defect recognition, and more convolution neural network algorithms are applied in this field. However, with the deepening of network layers, network degradation will occur. We propose a ResNet method for classifying metal surface defects. After experimental testing, we use ResNet34 to build an identification network. After training 300 epoch of the network using the NEU surface defect data set, the convergence of the network is very good. The accuracy of test set verification is 93.67% and higher than that of other surface defect recognition algorithms. Also, we can deepen the number of layers ResNet the network without worrying about network degradation.