Stealth sight: A multi perspective approach for camouflaged object detection

Abstract

Camouflaged object detection (COD) is a challenging task due to the inherent similarity between objects and their surroundings. This paper introduces Stealth Sight, a novel framework integrating multi-view feature fusion and depth-based refinement to enhance segmentation accuracy. Our approach incorporates a pretrained multi-view CLIP encoder and a depth extraction network, facilitating robust feature representation. Additionally, we introduce a cross-attention transformer decoder and a post-training pruning mechanism to improve efficiency. Extensive evaluations on benchmark datasets demonstrate that Stealth Sight outperforms state-of-the-art methods in camouflaged object segmentation. Our method significantly enhances detection in complex environments, making it applicable to medical imaging, security, and wildlife monitoring.