CAMOUFLAGED OBJECT DETECTION

The detection of camouflaged objects is crucial for applications in surveillance, wildlife monitoring, and military scenarios, where objects blend seamlessly into their surroundings. This review consolidates 15 influential research studies covering advancements in datasets, models, real-time detection technologies, and multimodal approaches. The focus is on implementing YOLOv8, a state-of-the-art real-time object detection model, using the ACD1K dataset, which is specifically designed for military surveillance. By synthesizing methodologies, evaluation metrics, and applications, this paper highlights significant progress in camouflaged object detection (COD) and identifies ongoing challenges in computational efficiency, dataset diversity, and real-world adaptability.