Target Classification via Fire Pattern Analysis in Infrared Imagery

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.