| Title |
A Method for Diagnosing Factory Facility Abnormality in CNN-transformer Network using Thermal Imaging |
| Authors |
김동현(Dong Hyun Kim) ; 황호성(Ho Seong Hwang) ; 김호철(Ho Chul Kim) |
| DOI |
https://doi.org/10.5573/ieie.2023.60.3.53 |
| Keywords |
CNN; Transformer; Thermal imaging; Abnormality diagnosis; Factory facility |
| Abstract |
In this paper, we propose a deep learning algorithm optimized for diagnosing factory facility abnormalities using thermal imaging. For this purpose, the contrast of the thermal image is clearly converted with the contrast enhancement algorithm to enhance the edge information. After that, the Convolution Vision Transformer (CvT) developed using only the advantages of Convolution Neural Network (CNN) and Transformer Network is modified to suit the diagnosis of thermal image-based failure facility abnormalities. Experiments were conducted by extracting normal and abnormal images of factory facilities from the thermal image provided by AI Hub. Through this, we confirmed the excellent performance of 98.79% which is higher accuracy than CNN-based ResNet, EfficientNet, and Transformer-based Vision Transformer (ViT), SwinT (Swin Transformer), which are commonly used in the existing computer vision field. In conclusion, it was confirmed that when using the CNN and Transformer fusion network, it shows better performance than the factory facility failure diagnosis algorithm using other thermal imaging images. |