Camouflaged Image Synthesis Is All You Need to Boost Camouflaged
  Detection
Haichao Zhang Can Qin Yu Yin Yun Fu
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.