The Development of Near Real Time Target Detection and Identification System for Ground Combat Vehicles via Artificial Intelligence

Abstract

Modern military intelligence systems are advancing rapidly, particularly in the classification of combat vehicles in battlefield environments. Accurately identifying the capabilities and vulnerabilities of these vehicles is crucial for developing effective tactics and operational strategies. The integration of artificial intelligence, specifically through image processing, pattern recognition, and deep learning techniques, has greatly enhanced the feasibility of military vehicle classification. This study employs the You Only Look Once (YOLO) algorithm, particularly the YOLOv8 model, which is optimized for mobile applications and has shown high performance in evaluations. Enhancements include the incorporation of a squeeze and excitation (SE) block, improving detection accuracy. The research emphasizes the importance of detecting detailed target characteristics using images collected from electro-optical systems and aims to bolster decision support processes in target management systems. The development of a deep learning-based Target Detection and Identification (TDI) system is outlined, encompassing specialized targeted data acquisition, pre-processing, image partitioning, feature extraction, classification, and recommendation generation through decision support matrices. This model significantly improves image processing and object detection capabilities. Notably, it facilitates passive identification of combat vehicles using existing electro-optical devices without the need for additional hardware, while estimating distances discreetly. The system delivers real-time predictions, achieving a mean Average Precision (mAP) of 88.38%, thereby facilitating informed decision-making and risk reduction in military operations.