HSDA: High-Frequency Shuffle Data Augmentation for Bird's-Eye-View Map Segmentation

Calvin Glisson, Qiuxiao Chen; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 8816-8825

Autonomous driving has garnered significant attention in recent research and Bird's-Eye-View (BEV) map segmentation plays a vital role in the field providing the basis for safe and reliable operation. While data augmentation is a commonly used technique for improving BEV map segmentation networks existing approaches predominantly focus on manipulating spatial domain representations. In this work we investigate the potential of frequency domain data augmentation for camera-based BEV map segmentation. We observe that high-frequency information in camera images is particularly crucial for accurate segmentation. Based on this insight we propose High-frequency Shuffle Data Augmentation (HSDA) a novel data augmentation strategy that enhances a network's ability to interpret high-frequency image content. This approach encourages the network to distinguish relevant high-frequency information from noise leading to improved segmentation results for small and intricate image regions as well as sharper edge and detail perception. Evaluated on the nuScenes dataset our method demonstrates broad applicability across various BEV map segmentation networks achieving a new state-of-the-art mean Intersection over Union (mIoU) of 61.3% for camera-only systems. This significant improvement underscores the potential of frequency domain data augmentation for advancing the field of autonomous driving perception. Code has been released: https://github.com/Zarhult/HSDA