본문 바로가기
  • Home

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
  • Abbr : JKSCI
  • 2024, 29(7), pp.41-51
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : April 25, 2024
  • Accepted : June 26, 2024
  • Published : July 31, 2024

Jae-Yong Baek 1 Dae-Hyeon Park 1 Hyuk-Jin Shin 2 Yong-Sang Yoo 1 Deok-Woong Kim 1 Du-Hwan Hur 1 SeungHwan Bae 3 Jun-Ho Cheon 3 Seung-Hwan Bae 1

1인하대학교
2인하대학교 인공지능융합연구센터
3LIG넥스원

Accredited

ABSTRACT

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%.

Citation status

* References for papers published after 2023 are currently being built.