Boundary enhancement and refinement network for camouflaged object detection

Camouflaged object detection aims to locate and segment objects accurately that conceal themselves well in the environment. Despite the advancements in deep learning methods, prevalent issues persist, including coarse boundary identification in complex scenes and the ineffective integration of multi...

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Bibliographic Details
Published inMachine vision and applications Vol. 35; no. 5; p. 107
Main Authors Xia, Chenxing, Cao, Huizhen, Gao, Xiuju, Ge, Bin, Li, Kuan-Ching, Fang, Xianjin, Zhang, Yan, Liang, Xingzhu
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2024
Springer Nature B.V
Subjects
Communications Engineering
Computer Science
Deep learning
Design
Engineering
Image Processing and Computer Vision
Localization
Medical research
Modules
Networks
Object recognition
Pattern Recognition
Refining
Semantics
State-of-the-art reviews
Feature aggregation
Boundary clues
Camouflaged object detection
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Summary:Camouflaged object detection aims to locate and segment objects accurately that conceal themselves well in the environment. Despite the advancements in deep learning methods, prevalent issues persist, including coarse boundary identification in complex scenes and the ineffective integration of multi-source features. To this end, we propose a novel boundary enhancement and refinement network named BERNet, which mainly consists of three modules for enhancing and refining boundary information: an asymmetric edge module (AEM) with multi-groups dilated convolution block (GDCB), a residual mixed pooling enhanced module (RPEM), and a multivariate information interaction refiner module (M2IRM). AEM with GDCB is designed to obtain rich boundary clues, where different dilation rates are used to expand the receptive field. RPEM is capable of enhancing boundary features under the guidance of boundary cues to improve the detection accuracy of small and multiple camouflaged objects. M2IRM is introduced to refine the side-out prediction maps progressively under the supervision of the ground truth by the fusion of multi-source information. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our BERNet with competitive state-of-the-art methods under the most evaluation metrics.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-024-01588-2