| Title |
Comparison of CNN Structures for Detection of Surface Defects |
| Authors |
최학영(Choi, Hakyoung) ; 서기성(Seo, Kisung) |
| DOI |
https://doi.org/10.5370/KIEE.2017.66.7.1100 |
| Keywords |
Surface defects detection ; Variance of variance ; Convolution neural network ; Deep learning |
| Abstract |
A detector-based approach shows the limited performances for the defect inspections such as shallow fine cracks and indistinguishable defects from background. Deep learning technique is widely used for object recognition and it's applications to detect defects have been gradually attempted. Deep learning requires huge scale of learning data, but acquisition of data can be limited in some industrial application. The possibility of applying CNN which is one of the deep learning approaches for surface defect inspection is investigated for industrial parts whose detection difficulty is challenging and learning data is not sufficient. VOV is adopted for pre-processing and to obtain a resonable number of ROIs for a data augmentation. Then CNN method is applied for the classification. Three CNN networks, AlexNet, VGGNet, and mofified VGGNet are compared for experiments of defects detection. |