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arXiv · 2308.06701

Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection

Haichao Zhang·Can Qin·Yu Yin·Yun Fu
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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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Haichao Zhang, Can Qin, Yu Yin, Yun Fu. 2023-08-13. Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection. https://arxiv.org/abs/2308.06701

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