This study introduces a challenge faced by CNN in the task of traffic sign detection: how to achieve robustness to distributional shift. At present, all kinds of CNN models rely on strong data augmentation methods to enrich samples and achieve robustness, such as Mosaic and Mixup. In this study, we note that these methods do not have similar effects on combating noise. We explore the performance of augmentation strategies against disturbance in different frequency bands and provide understanding from the Fourier analysis perspective. This understanding can provide a guidance for selecting data augmentation strategies for different detection tasks and benchmark datasets.