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Analysis of the Region of Interest According to CNN Structure in Hierarchical Pattern Surface Inspection Using CAM
- Source :
- Materials, Vol 14, Iss 9, p 2095 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- A convolutional neural network (CNN), which exhibits excellent performance in solving image-based problem, has been widely applied to various industrial problems. In general, the CNN model was applied to defect inspection on the surface of raw materials or final products, and its accuracy also showed better performance compared to human inspection. However, surfaces with heterogeneous and complex backgrounds have difficulties in separating defects region from the background, which is a typical challenge in this field. In this study, the CNN model was applied to detect surface defects on a hierarchical patterned surface, one of the representative complex background surfaces. In order to optimize the CNN structure, the change in inspection performance was analyzed according to the number of layers and kernel size of the model using evaluation metrics. In addition, the change of the CNN’s decision criteria according to the change of the model structure was analyzed using a class activation map (CAM) technique, which can highlight the most important region recognized by the CNN in performing classification. As a result, we were able to accurately understand the classification manner of the CNN for the hierarchical pattern surface, and an accuracy of 93.7% was achieved using the optimized model.
- Subjects :
- class activation map
convolutional neural network
hierarchical pattern
region of interest
surface inspection
Technology
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Engineering (General). Civil engineering (General)
TA1-2040
Microscopy
QH201-278.5
Descriptive and experimental mechanics
QC120-168.85
Subjects
Details
- Language :
- English
- ISSN :
- 19961944
- Volume :
- 14
- Issue :
- 9
- Database :
- Directory of Open Access Journals
- Journal :
- Materials
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.8e85995dded14948a4eddc20a8e2daf9
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/ma14092095