Dual-optimized two-stage Camouflaged Object Detection

doi:10.1016/j.jvcir.2025.104631

Dual-optimized two-stage Camouflaged Object Detection

2025-11-07

0

PRE

AI

S

Sanxin Jiang *

张

张宏亮 (Hongliang Zhang)

C

Changde Ding

DOI：10.1016/j.jvcir.2025.104631

Original

Original request for help

Save

Abstract

En 中文

To address the current issues of inaccurate object localization and insufficient edge information extraction in Camouflaged Object Detection (COD), inspired by how humans detect camouflaged objects—first identifying their general outline and then focusing on finer details—we propose a novel two-stage network, DONet, for COD. In the first stage, the network leverages an Edge Exploration Module (EEM) to locate object boundaries, refining this boundary information through Retrieve Attention. Subsequently, the Object Position Recognition Module (OPRM) detects the horizontal and vertical locations of camouflaged objects by integrating boundary information with high-level features. This information is further enhanced by combining multi-dilation channels and neighboring features. In the second stage, a Context Aggregation Module (CAM) is used to aggregate contextual information, improving detection accuracy. Extensive experiments demonstrate that DONet surpasses 16 state-of-the-art methods across three challenging datasets, highlighting its effectiveness and superior performance.In addition, DONet also has outstanding detection performance in the field of medical polyp segmentation.

Dual-optimized two-stage Camouflaged Object Detection

Abstract

Journal

Organization