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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