TANet: Tri-Aspects Network for Camouflaged Object Detection

Recent advances in computer vision have introduced generalized image segmentation models applicable across various domains. However, camouflaged object detection (COD) remains a particularly challenging task that requires dedicated approaches, owing to the minimal visual distinction between objects and their backgrounds. The degree of camouflage is influenced by three critical aspects—color, texture, and edge—requiring methodologies that address these simultaneously. Efforts to detect camouflaged objects have continually focused on these aspects. In this study, we propose the Tri-Aspects Network (TANet) for COD, designed to overcome the limitations of existing approaches that primarily focus on a single aspect. TANet emphasizes differences in color, texture, and edge to detect camouflaged objects. It consists of an ensemble of two independent networks that learn from the color differences extracted through color conversion and the textural features extracted using Bayar convolution filters. Each independent network enhances high-level features extracted from the input image through the Context Enhancement Block (CEB) and maximizes the difference between the background and camouflaged objects during reconstruction with the prediction mask using the Multi-scale Edge Refinement Block (MERB). The results from these two networks are then ensembled. Additionally, by using an erosion kernel to ensure that the prediction mask’s edge closely matches the ground truth edge, more fine-grained predictions can be achieved. TANet proposes a novel COD network that shows outstanding results compared to existing models in three key evaluation metrics (S-measure, E-measure, and weighted F-score), demonstrating its contribution to the field.