@article{ART003104508},
author={Jae-Yong Baek and Dae-Hyeon Park and Hyuk-Jin Shin and Yong-Sang Yoo and Deok-Woong Kim and Du-Hwan Hur and SeungHwan Bae and Jun-Ho Cheon and Seung-Hwan Bae},
title={Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={7},
pages={41-51},
doi={10.9708/jksci.2024.29.07.041}
TY - JOUR
AU - Jae-Yong Baek
AU - Dae-Hyeon Park
AU - Hyuk-Jin Shin
AU - Yong-Sang Yoo
AU - Deok-Woong Kim
AU - Du-Hwan Hur
AU - SeungHwan Bae
AU - Jun-Ho Cheon
AU - Seung-Hwan Bae
TI - Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 7
PB - The Korean Society Of Computer And Information
SP - 41
EP - 51
SN - 1598-849X
AB - In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them.
In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.
KW - Guided weapons;Infrared imaging;Deep learning;Object detection;Image pre-processing
DO - 10.9708/jksci.2024.29.07.041
ER -
Jae-Yong Baek, Dae-Hyeon Park, Hyuk-Jin Shin, Yong-Sang Yoo, Deok-Woong Kim, Du-Hwan Hur, SeungHwan Bae, Jun-Ho Cheon and Seung-Hwan Bae. (2024). Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection. Journal of The Korea Society of Computer and Information, 29(7), 41-51.
Jae-Yong Baek, Dae-Hyeon Park, Hyuk-Jin Shin, Yong-Sang Yoo, Deok-Woong Kim, Du-Hwan Hur, SeungHwan Bae, Jun-Ho Cheon and Seung-Hwan Bae. 2024, "Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection", Journal of The Korea Society of Computer and Information, vol.29, no.7 pp.41-51. Available from: doi:10.9708/jksci.2024.29.07.041
Jae-Yong Baek, Dae-Hyeon Park, Hyuk-Jin Shin, Yong-Sang Yoo, Deok-Woong Kim, Du-Hwan Hur, SeungHwan Bae, Jun-Ho Cheon, Seung-Hwan Bae "Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection" Journal of The Korea Society of Computer and Information 29.7 pp.41-51 (2024) : 41.
Jae-Yong Baek, Dae-Hyeon Park, Hyuk-Jin Shin, Yong-Sang Yoo, Deok-Woong Kim, Du-Hwan Hur, SeungHwan Bae, Jun-Ho Cheon, Seung-Hwan Bae. Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection. 2024; 29(7), 41-51. Available from: doi:10.9708/jksci.2024.29.07.041
Jae-Yong Baek, Dae-Hyeon Park, Hyuk-Jin Shin, Yong-Sang Yoo, Deok-Woong Kim, Du-Hwan Hur, SeungHwan Bae, Jun-Ho Cheon and Seung-Hwan Bae. "Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection" Journal of The Korea Society of Computer and Information 29, no.7 (2024) : 41-51.doi: 10.9708/jksci.2024.29.07.041
Jae-Yong Baek; Dae-Hyeon Park; Hyuk-Jin Shin; Yong-Sang Yoo; Deok-Woong Kim; Du-Hwan Hur; SeungHwan Bae; Jun-Ho Cheon; Seung-Hwan Bae. Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection. Journal of The Korea Society of Computer and Information, 29(7), 41-51. doi: 10.9708/jksci.2024.29.07.041
Jae-Yong Baek; Dae-Hyeon Park; Hyuk-Jin Shin; Yong-Sang Yoo; Deok-Woong Kim; Du-Hwan Hur; SeungHwan Bae; Jun-Ho Cheon; Seung-Hwan Bae. Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection. Journal of The Korea Society of Computer and Information. 2024; 29(7) 41-51. doi: 10.9708/jksci.2024.29.07.041
Jae-Yong Baek, Dae-Hyeon Park, Hyuk-Jin Shin, Yong-Sang Yoo, Deok-Woong Kim, Du-Hwan Hur, SeungHwan Bae, Jun-Ho Cheon, Seung-Hwan Bae. Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection. 2024; 29(7), 41-51. Available from: doi:10.9708/jksci.2024.29.07.041
Jae-Yong Baek, Dae-Hyeon Park, Hyuk-Jin Shin, Yong-Sang Yoo, Deok-Woong Kim, Du-Hwan Hur, SeungHwan Bae, Jun-Ho Cheon and Seung-Hwan Bae. "Research on Local and Global Infrared Image Pre-Processing Methods for Deep Learning Based Guided Weapon Target Detection" Journal of The Korea Society of Computer and Information 29, no.7 (2024) : 41-51.doi: 10.9708/jksci.2024.29.07.041
