@article{ART003197387},
author={Hye-Won Kim and Sang-Min Kim and Jung-Mo Sohn},
title={Anomaly Detection in IR-Camera Using Deep Learning},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2025},
volume={30},
number={4},
pages={1-9},
doi={10.9708/jksci.2025.30.04.001}
TY - JOUR
AU - Hye-Won Kim
AU - Sang-Min Kim
AU - Jung-Mo Sohn
TI - Anomaly Detection in IR-Camera Using Deep Learning
JO - Journal of The Korea Society of Computer and Information
PY - 2025
VL - 30
IS - 4
PB - The Korean Society Of Computer And Information
SP - 1
EP - 9
SN - 1598-849X
AB - In this paper, we propose an infrared camera anomaly detection system based on deep learning. In general industrial sites, infrared cameras are used for facility management. However, if something goes wrong with the infrared camera, recording stops and the status of the facility is unknown. Therefore, monitoring is necessary to prepare for this. Currently, only manpower is monitoring the camera.
However, there are limitations such as human error and mass inspection. To solve this limitation, we compare performance using AlexNet, VGGNet, ResNet, which are types of deep learning, and propose an automated monitoring method with better performance by comparing the captured original image and the histogram extracted image as a preprocessing process. The data set was collected and used by KEPCO's infrared night vision camera, and the results of training the data set with the VGGNet model and predicting the results were the best. The result is that the input image is judged to be normal/abnormal for the image captured at a certain period, and the model performance came out as Accuracy 0.97, Precision 0.97, Recall 0.97, F1-score 0.97. In addition, it was confirmed that the data set preprocessed with the histogram performed better.
KW - IR-Camera;Anomaly Detection;Monitoring;Deep Learning;VGGNet
DO - 10.9708/jksci.2025.30.04.001
ER -
Hye-Won Kim, Sang-Min Kim and Jung-Mo Sohn. (2025). Anomaly Detection in IR-Camera Using Deep Learning. Journal of The Korea Society of Computer and Information, 30(4), 1-9.
Hye-Won Kim, Sang-Min Kim and Jung-Mo Sohn. 2025, "Anomaly Detection in IR-Camera Using Deep Learning", Journal of The Korea Society of Computer and Information, vol.30, no.4 pp.1-9. Available from: doi:10.9708/jksci.2025.30.04.001
Hye-Won Kim, Sang-Min Kim, Jung-Mo Sohn "Anomaly Detection in IR-Camera Using Deep Learning" Journal of The Korea Society of Computer and Information 30.4 pp.1-9 (2025) : 1.
Hye-Won Kim, Sang-Min Kim, Jung-Mo Sohn. Anomaly Detection in IR-Camera Using Deep Learning. 2025; 30(4), 1-9. Available from: doi:10.9708/jksci.2025.30.04.001
Hye-Won Kim, Sang-Min Kim and Jung-Mo Sohn. "Anomaly Detection in IR-Camera Using Deep Learning" Journal of The Korea Society of Computer and Information 30, no.4 (2025) : 1-9.doi: 10.9708/jksci.2025.30.04.001
Hye-Won Kim; Sang-Min Kim; Jung-Mo Sohn. Anomaly Detection in IR-Camera Using Deep Learning. Journal of The Korea Society of Computer and Information, 30(4), 1-9. doi: 10.9708/jksci.2025.30.04.001
Hye-Won Kim; Sang-Min Kim; Jung-Mo Sohn. Anomaly Detection in IR-Camera Using Deep Learning. Journal of The Korea Society of Computer and Information. 2025; 30(4) 1-9. doi: 10.9708/jksci.2025.30.04.001
Hye-Won Kim, Sang-Min Kim, Jung-Mo Sohn. Anomaly Detection in IR-Camera Using Deep Learning. 2025; 30(4), 1-9. Available from: doi:10.9708/jksci.2025.30.04.001
Hye-Won Kim, Sang-Min Kim and Jung-Mo Sohn. "Anomaly Detection in IR-Camera Using Deep Learning" Journal of The Korea Society of Computer and Information 30, no.4 (2025) : 1-9.doi: 10.9708/jksci.2025.30.04.001
