Deep Learning Advances in Camouflaged Object Detection

Camouflaged Object Detection (COD) in computer vision is a complex task, focusing on identifying and delineating subjects that closely resemble their surroundings. The method finds extensive use across various sectors, including defense, health care, farming, and emergency services. This study provides an overview of deep learning approaches for detecting camouflaged targets, encompassing diverse strategies like bio-inspired techniques, feature integration, multi-task training, multi-modal data synthesis, progressive refinement, probabilistic modeling, and novel loss function designs. Then this paper discusses in detail the various deep-learning models and introduces the challenges of camouflage target detection. Several commonly used camouflaged target detection datasets are also analyzed and key metrics for evaluating model performance are discussed. Through organization and analysis, this paper aims to provide a broad perspective to help researchers advance and implement camouflaged target detection techniques.