Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection
This work addresses data scarcity in camouflaged object detection, a domain-specific computer vision task, with incremental improvements through data synthesis.
The paper tackles the problem of limited data availability for camouflaged object detection by proposing a framework to synthesize realistic camouflage images, which improves detection performance, outperforming state-of-the-art methods on three datasets (COD10k, CAMO, and CHAMELEON).
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.