文章基本信息

标题：Surface Flaw Detection of Industrial Products Based on Convolutional Neural Network
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作者：Yongjun Zhang ; Ziliang Wang
期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：252
期号：2
页码：1-6
DOI：10.1088/1755-1315/252/2/022114
出版社：IOP Publishing
摘要：Surface flaw detection in industrial products is a typical application of image classification. By improving the structure of Convolutional Neural Network (CNN), for example, the first large-scale convolution kernel is replaced by a cascaded 3×3 convolution kernel; replaces the whole with a 1×1 convolution kernel and Global Average Pooling Connection layer; sets the appropriate batch_size, the convergence rate and convergence accuracy of the model are greatly improved. Experiments show that the proposed method has a classification accuracy of more than 96% in the detection of automotive hose surface flaws.