Camouflaged object detection is an important and challenging task which aims to accurately detect tar- gets which are“ perfectly ”hidden in the surrounding environment. Currently , camouflaged object detection has attrac- ted widespread attention in the field of computer vision , and scholars have successfully proposed various types of detec- tion models. However , most of the work is aimed at building efficient detection models , and there is a lack of in-depth analysis and generalization of existing models. Therefore , this paper presents a comprehensive analysis and summary of existing camouflaged object detection models and discusses potential research directions for camouflaged object detec- tion. Firstly , an overall review of the existing models is given in two broad categories , traditional methods and deep learning-based methods , and the principles , advantages and disadvantages of the relevant models are elaborated ; Sec- ondly , common datasets and evaluation metrics in the field of camouflaged object detection are introduced ; Then , ex- isting deep learning-based camouflaged object detection models are reproduced , and the detection results of different types of models on public datasets are compared in both qualitative and quantitative terms ; Finally , the whole paper is summarized and future research directions in the field of camouflaged object detection are prospected.