Utilizing YOLO V8 for Camouflaged Military Soldier Density Estimation
Accurate estimation of camouflaged military soldier density is vital for strategic planning and operational efficiency in military operations. Recent studies indicate a growing challenge in detecting camouflaged personnel in diverse environments, emphasizing the need for advanced techniques. This project employs YOLO V8, an enhanced object detection model renowned for its speed and accuracy, to effectively identify and count camouflaged soldiers in real time. YOLO V8 utilizes a sophisticated architecture that includes features like improved feature extraction and better handling of small objects, making it particularly suited for challenging detection tasks. By integrating Non-Maximum Suppression (NMS), the model enhances counting accuracy by filtering out redundant detections and retaining only the most reliable instances. The proposed methodology not only supports immediate tactical responses but also aids in long-term military strategy development. To assess model performance, we utilize metrics such as F1 Score, Precision, Recall and Accuracy offering a detailed evaluation of detection accuracy and robustness.
Hugging face : https://huggingface.co/spaces/theesmarty/Camoufalge-Soldier-Density-Estimation/tree/main