通过高频混洗增强，提升摄像头感知的鸟瞰图地图分割精度。
HSDA: High-frequency Shuffle Data Augmentation for Bird's-Eye-View Map Segmentation
- 在频域对图像高频成分进行随机混洗，增强模型对细节的感知能力。
- 在nuScenes数据集上实现61.3%的mIoU，刷新纯摄像头系统新纪录。
- 特别适合提升小目标和边缘区域的分割表现，适合自动驾驶感知研究者。
自动驾驶近年来备受关注，鸟瞰图（BEV）地图分割在此领域至关重要，为安全可靠运行提供基础。尽管数据增强常用于提升BEV地图分割网络性能，现有方法多集中于空间域操作。本文探究基于摄像头的BEV地图分割中频域数据增强的潜力。观察发现，图像中的高频信息对精确分割尤为关键。基于此，提出高频混洗数据增强（HSDA），一种新颖的数据增强策略，可提升网络对高频内容的解析能力。该方法促使网络区分有效高频信息与噪声，从而改善小而复杂的图像区域分割效果，并增强边缘与细节感知。在nuScenes数据集上的评估表明，该方法在多种BEV地图分割网络中均具广泛适用性，使纯摄像头系统达到61.3%的mIoU，创下新纪录。这一显著提升凸显了频域数据增强在自动驾驶感知领域的巨大潜力。代码已开源：https://github.com/Zarhult/HSDA。
原文摘要 · Abstract (English)
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
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