Utilizing YOLO V8 for Camouflaged Military Soldier Density Estimation
Accurately estimating the density of camouflaged military personnel is critical for effective strategic planning and operational efficiency. Detecting camouflaged soldiers in diverse environments has become increasingly challenging, highlighting the need for advanced detection techniques. This project utilizes YOLO V8, a cutting-edge object detection model known for its speed and precision, to identify and count camouflaged soldiers in real time. YOLO V8's architecture includes improved feature extraction and enhanced handling of small objects, making it ideal for this complex task. By incorporating Non-Maximum Suppression (NMS), the model reduces redundant detections and retains only the most reliable ones, improving counting accuracy. This methodology supports both immediate tactical decisions and long-term military strategy. Performance evaluation is conducted using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), ensuring a comprehensive assessment of detection accuracy and model robustness.