Work

Target Classification via Fire Pattern Analysis in Infrared Imagery

Year: 2025

Type: article

Abstract: To carry out missions effectively in battlefield environments, it is essential to rapidly identify enemy threats and respond through precise analysis. Accordingly, technologies that utilize artificial intelligence to identify and classify targets in real time are being actively researched in modern warfare. In this study, we propose a deep learning-based target classification algorithm that simultaneously ensures real-time performance and high classification accuracy. Time-series data are constructed from infrared imagery and augmented to train the model, and the effectiveness of the proposed algorithm is demonstrated through comparative experiments with various CNN- and RNN-based models.

Source: Journal of the Korea Institute of Military Science and Technology

DOI: 10.9766/kimst.2025.28.5.486

Authors: Dae-hyeok Kwon, Minkyu Shin, Sky Haneul Lee, Han-Lim Choi, Jaeuk Kim, Mingi Kim

Institution: Korea Advanced Institute of Science and Technology

Language: English

Topic: Infrared Target Detection Methodologies

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: Korea Research Institute for Defense Technology Planning and Advancement, Defense Acquisition Program Administration

Award: G8012970249