In military applications, detecting camouflaged objects is a challenging task due to the ability of targets to blend seamlessly into their surroundings. This study investigates the impact of style transfer approaches on synthetic data generation and their effectiveness in improving camouflaged object detection. By utilizing style transfer techniques, we augment existing datasets with synthetic imagery that mimics various environmental textures and conditions. The goal is to enhance the training of detection models, enabling them to better recognize camouflaged objects under diverse operational scenarios. Experimental results demonstrate that style-transfer-based data augmentation improves detection accuracy and robustness in military camouflaged object detection systems. This research highlights the potential of style transfer for augmenting training data in detecting camouflaged military objects.