The Role of YOLOv8 in Enhancing Strategic Military Equipment Detection

The accurate and fast identification of military equipment on the battlefield is a crucial problem of various military missions and tasks. First, it affects the operation success of the modern armed forces. Second, decision-making is considerably affected by such a capability. In this paper, we address this issue using the potential of the powerful YOLOv8 technique. This allows us to offer an accurate model for identifying various components related to the military and armed forces. The model is tested on the publicly available dataset which contains 11,800 images by evaluating the detection task with YOLOv8-based models. This dataset consists of eleven classes on military-related equipment, which covers different types of military assets. Finally, the YOLOv8x model shows encouraging results in detecting military equipment as demonstrated by a value of 99.1% in terms of mAP50. Through the findings of this study, there has been significant improvement in using AI in military missions, decision-making, and mission readiness which are better than those obtained in the previous studies. According to the obtained findings, this study demonstrates substantial progress in the application of artificial intelligence to military missions and decision-making and operational development.