A Study on the Construction of a Tank Dataset for Fine-grained Classification and the Classification Performance by Pre-processing

In future battlefields, unmanned systems will play a crucial role in collecting visual intelligence, which enables fast and accurate decision-making by operators and commanders. Among various data types, visual information often offers the most intuitive and reliable insights. The level of analysis of such intelligence especially the granularity of object classification can significantly influence tactical and strategic decisions. Fine-Grained Visual Classification(FGVC), which enables model-level identification(e.g. distinguishing a “T-55 tank” from a generic “armored vehicle”), is essential for achieving information superiority. However, the defense industry faces significant challenges in acquiring high-quality training data due to issues of security, scarcity, and limited diversity. To address this, the present study introduces a fine-grained labeled image dataset containing 34 distinct tank models currently in operation by key countries around the Korean Peninsula. The dataset includes over 4,400 images captured under various conditions to support robust training of AI models. We validate its effectiveness by fine-tuning an Inception-v4 model and analyzing performance across multiple pre-processing methods. This work is expected to support the advancement of automatic target recognition, situational awareness, and AI-based combat simulations within MUM-T frameworks, contributing to smarter and more resilient defense systems.