A style transfer-based augmentation approach for detecting military camouflaged object

Detecting camouflaged objects in military environments is particularly challenging due to the deliberate blending of targets with their surroundings. Despite advancements in deep learning, the limited availability of training data remains a significant obstacle. To address this issue, this paper proposes a novel data augmentation approach that combines multiple style transfer models to transform the style of training images into diverse reference styles. This enriches the training data by simulating various environmental textures and patterns. Structural Similarity (SSIM) is used as an evaluation metric to select style-transferred images that preserve the best structural similarity, enabling detection models to more effectively learn the distinguishing features of camouflaged objects. Extensive experiments with different style transfer methods and SSIM thresholds show that our augmentation approach significantly enhances the accuracy of state-of-the-art detection algorithms. This approach has the potential to improve object detection in military operations, increasing the reliability and precision of automated surveillance systems.