A systematic review of image-level camouflaged object detection with deep learning

Camouflaged object detection (COD) aims to search and identify disguised objects that are hidden in their surrounding environment, thereby deceiving the human visual system. As an interesting and challenging task, COD has received increasing attention from the community in the past few years, especially for image-level camouflaged object segmentation task. So far, some advanced image-level COD models have been proposed, mainly dominated by deep learning-based solutions. To have an in-depth understanding of existing image-level COD methods in the deep learning era, in this paper, we give a comprehensive review on model structure and paradigm classification, public benchmark datasets, evaluation metrics, model performance benchmark, and potential future development directions. Specifically, we first review 96 existing deep COD algorithms. Subsequently, we summarize and analyze the existing five widely used COD datasets and evaluation metrics. Furthermore, we benchmark a set of representative models and provide a detailed analysis of the comparison results from both quantitative and qualitative perspectives. Moreover, we further discuss the challenges of COD and the corresponding solutions. Finally, based on the understanding of this field, future development trends and potential research directions are prospected. In conclusion, the purpose of this paper is to provide researchers with a review of the latest COD methods, increase their understanding of COD research, and gain some enlightenment.