AI_Camouflage_Detection
An experimental computer vision system that analyzes images to identify potential camouflaged objects using object detection, edge detection, and texture analysis with Python, OpenCV, and YOLOv8.
AI Camouflage Detection is a computer vision project designed to explore techniques for detecting objects that blend into their surroundings. Camouflaged objects are difficult to identify because they intentionally reduce color contrast and visual boundaries with the background. This project experiments with AI-based image analysis methods to highlight potentially hidden or camouflaged regions in images.
The system uses YOLOv8 for object detection combined with OpenCV-based image processing techniques such as edge detection and texture analysis. These methods help reveal subtle patterns, shapes, or anomalies that may indicate the presence of camouflaged objects.
The project serves as a prototype for applications in areas such as security surveillance, wildlife monitoring, search and rescue operations, and military reconnaissance, where detecting hidden objects is important.
The implementation is built using Python, PyTorch, OpenCV, and the Ultralytics YOLO framework, and the repository demonstrates the basic pipeline for analyzing images and identifying suspicious regions that may contain camouflaged objects.