A survey on deep learning-based camouflaged object detection

Abstract

Camouflaged object detection (COD) is an emerging visual detection task that aims to identify objects that conceal themselves in the surrounding environment. The high intrinsic similarities between the camouflaged objects and their backgrounds make COD far more challenging than traditional object detection. Recently, COD has attracted increasing research interest in the computer vision community, and numerous deep learning-based methods have been proposed, showing great potential. However, most of the existing work focuses on analyzing the structure of COD models, with few overview works summarizing deep learning-based models. To address this gap, we provide a comprehensive analysis and summary of deep learning-based COD models. Specifically, we first classify 48 deep learning-based COD models and analyze their advantages and disadvantages. Second, we introduce widely available datasets for COD and performance evaluation metrics. Then, we evaluate the performance of existing deep learning-based COD models on these four datasets. Finally, we indicate relevant applications and discuss challenges and future research directions for the COD task.