Military Aircraft Detection Using YOLOv5

Abstract

In making strategic decisions in the military, military aircraft detection has become increasingly crucial. The identification of military aircraft continues to be a problematic issue. In operations and wars, military aircraft detection is crucial for detecting unknown aircraft. The difficulty is always in accurately recognizing the unfamiliar aircraft, regardless of class and orientation. Military aircraft, such as stealth aircraft, are still difficult to detect because stealth aircraft are more challenging to detect or track using conventional radar. Still, these aircraft can be detected using object detection. In this article, we proposed identifying five types of airplanes independent of class or direction using object detection. The You Only Look Once version 5 (YOLOv5) method and the PyTorch military aircraft dataset were used to identify various aircraft. The identification of different aircraft was discovered using the YOLOv5 algorithm and the PyTorch military aircraft dataset. Bounding boxes for the dataset, data pre-processing, and data augmentation are made using Roboflow. The goal is to employ computer vision and object identification to identify whether a particular aircraft is a military aircraft. This military aircraft detection may be used in the border area, air force, and marine force.