Camouflaged Image Synthesis Is All You Need to Boost Camouflaged
Detection
- URL: http://arxiv.org/abs/2308.06701v1
- Date: Sun, 13 Aug 2023 06:55:05 GMT
- Title: Camouflaged Image Synthesis Is All You Need to Boost Camouflaged
Detection
- Authors: Haichao Zhang, Can Qin, Yu Yin, Yun Fu
- Abstract summary: We propose a framework for synthesizing camouflage data to enhance the detection of camouflaged objects in natural scenes.
Our approach employs a generative model to produce realistic camouflage images, which can be used to train existing object detection models.
Our framework outperforms the current state-of-the-art method on three datasets.
- Score: 65.8867003376637
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Camouflaged objects that blend into natural scenes pose significant
challenges for deep-learning models to detect and synthesize. While camouflaged
object detection is a crucial task in computer vision with diverse real-world
applications, this research topic has been constrained by limited data
availability. We propose a framework for synthesizing camouflage data to
enhance the detection of camouflaged objects in natural scenes. Our approach
employs a generative model to produce realistic camouflage images, which can be
used to train existing object detection models. Specifically, we use a
camouflage environment generator supervised by a camouflage distribution
classifier to synthesize the camouflage images, which are then fed into our
generator to expand the dataset. Our framework outperforms the current
state-of-the-art method on three datasets (COD10k, CAMO, and CHAMELEON),
demonstrating its effectiveness in improving camouflaged object detection. This
approach can serve as a plug-and-play data generation and augmentation module
for existing camouflaged object detection tasks and provides a novel way to
introduce more diversity and distributions into current camouflage datasets.
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