Boundary-Guided Camouflaged Object Detection

TL;DR

This paper introduces BGNet, a boundary-guided network that leverages edge semantics to improve the accuracy of camouflaged object detection, especially in boundary localization, outperforming existing methods.

Contribution

The paper proposes a novel boundary-guided network that utilizes edge semantics to enhance feature learning for more accurate camouflaged object detection.

Findings

Outperforms 18 state-of-the-art methods on benchmark datasets

Significantly improves boundary localization accuracy

Achieves higher scores across multiple evaluation metrics

Abstract

Camouflaged object detection (COD), segmenting objects that are elegantly blended into their surroundings, is a valuable yet challenging task. Existing deep-learning methods often fall into the difficulty of accurately identifying the camouflaged object with complete and fine object structure. To this end, in this paper, we propose a novel boundary-guided network (BGNet) for camouflaged object detection. Our method explores valuable and extra object-related edge semantics to guide representation learning of COD, which forces the model to generate features that highlight object structure, thereby promoting camouflaged object detection of accurate boundary localization. Extensive experiments on three challenging benchmark datasets demonstrate that our BGNet significantly outperforms the existing 18 state-of-the-art methods under four widely-used evaluation metrics. Our code is publicly available at: https://github.com/thograce/BGNet.