A bioinspired three-stage model for camouflaged object detection
- URL: http://arxiv.org/abs/2305.12635v2
- Date: Tue, 6 Jun 2023 12:17:15 GMT
- Title: A bioinspired three-stage model for camouflaged object detection
- Authors: Tianyou Chen, Jin Xiao, Xiaoguang Hu, Guofeng Zhang, Shaojie Wang
- Abstract summary: We propose a three-stage model that enables coarse-to-fine segmentation in a single iteration.
Our model employs three decoders to sequentially process subsampled features, cropped features, and high-resolution original features.
Our network surpasses state-of-the-art CNN-based counterparts without unnecessary complexities.
- Score: 8.11866601771984
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Camouflaged objects are typically assimilated into their backgrounds and
exhibit fuzzy boundaries. The complex environmental conditions and the high
intrinsic similarity between camouflaged targets and their surroundings pose
significant challenges in accurately locating and segmenting these objects in
their entirety. While existing methods have demonstrated remarkable performance
in various real-world scenarios, they still face limitations when confronted
with difficult cases, such as small targets, thin structures, and indistinct
boundaries. Drawing inspiration from human visual perception when observing
images containing camouflaged objects, we propose a three-stage model that
enables coarse-to-fine segmentation in a single iteration. Specifically, our
model employs three decoders to sequentially process subsampled features,
cropped features, and high-resolution original features. This proposed approach
not only reduces computational overhead but also mitigates interference caused
by background noise. Furthermore, considering the significance of multi-scale
information, we have designed a multi-scale feature enhancement module that
enlarges the receptive field while preserving detailed structural cues.
Additionally, a boundary enhancement module has been developed to enhance
performance by leveraging boundary information. Subsequently, a mask-guided
fusion module is proposed to generate fine-grained results by integrating
coarse prediction maps with high-resolution feature maps. Our network surpasses
state-of-the-art CNN-based counterparts without unnecessary complexities. Upon
acceptance of the paper, the source code will be made publicly available at
https://github.com/clelouch/BTSNet.
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