Development of "Target Detection and Identification System" for Combat Military Vehicles via Artificial Intelligence and Decision Support Matrix

Modern military intelligence systems are improving day by day. In the context of ground forces, combat vehicle-type classification technology serves as a valuable supporting asset. Understanding the capabilities and limitations of different combat vehicle types is crucial for developing effective tactics and strategies. The implementation of artificial intelligence-supported military vehicle type classification technology has raised increasing concerns as image processing, pattern recognition, and deep learning have advanced. You Only Look Once (YOLO) has demonstrated numerous considerable advantages in object detection and image classification. This method accelerates target detection because it can predict objects in real-time. The high accuracy of detection and assessment assists operational personnel in the field. The YOLO prediction method produces accurate results with minimal background errors and facilitates the understanding of generalized object representations. This paper utilizes YOLO to demonstrate combat vehicle type detection, specifically employing the YOLOv8m model, which is well-suited for mobile deployments. This study focuses on a process that begins with detecting targets using images obtained through electro-optical systems, aimed at supporting the activities of identifying and defining targets, which are essential components of target management systems. The process involves developing a Target Detection and Identification (TDI) system based on deep learning, incorporating steps such as pre-processing, segmentation, feature extraction, classification, additional data extraction, and decision support. This system is designed to interpret the results obtained from these processes and provide actionable recommendations. Furthermore, it assists in addressing the weapon target assignment problem in the land environment.