IdeNet: Making Neural Network Identify Camouflaged Objects Like Creatures

Abstract

Camouflaged objects often blend in with their surroundings, making the perception of a camouflaged object a more complex procedure. However, most neural-network-based methods that simulate the visual information processing pathway of creatures only roughly define the general process, which deficiently reproduces the process of identifying camouflaged objects. How to make modeled neural networks perceive camouflaged objects as effectively as creatures is a significant topic that deserves further consideration. After meticulous analysis of biological visual information processing, we propose an end-to-end prudent and comprehensive neural network, termed IdeNet, to model the critical information processing. Specifically, IdeNet divides the entire perception process into five stages: information collection, information augmentation, information filtering, information localization, and information correction and object identification. In addition, we design tailored visual information processing mechanisms for each stage, including the information augmentation module (IAM), the information filtering module (IFM), the information localization module (ILM), and the information correction module (ICM), to model the critical visual information processing and establish the inextricable association of biological behavior and visual information processing. The extensive experiments show that IdeNet outperforms state-of-the-art methods in all benchmarks, demonstrating the effectiveness of the five-stage partitioning of visual information processing pathway and the tailored visual information processing mechanisms for camouflaged object detection. Our code is publicly available at: https://github.com/whyandbecause/IdeNet .