DEVELOPING A DEEP LEARNING MODEL FOR DETECTING CAMOUFLAGED MILITARY OBJECTS BASED ON THE YOLOV9 MODEL

Abstract

Detecting camouflaged military objects is a significant challenge as they are typically designed to blend into their surroundings. This paper proposes an automated method for detecting military camouflage using deep learning techniques, specifically the YOLOv9 (You Only Look Once) model. YOLOv9 is one of the advanced object detection models, renowned for its real-time processing capabilities and high accuracy. The YOLOv9 model was trained and evaluated on a specialized dataset, including images containing camouflaged military objects in various contexts. Experimental results show that the YOLOv9 model achieves high performance in detecting camouflaged military objects, with superior accuracy compared to traditional methods. This paper not only demonstrates the feasibility of applying deep learning to camouflage detection but also opens up new directions for research and applications in this field.