对比CNN与ViT在事件相机下的抗噪能力，发现ViT更稳定。
From Ground to Air: Noise Robustness in Vision Transformers and CNNs for Event-Based Vehicle Classification with Potential UAV Applications
- 用ResNet34和ViT B16处理事件数据，评估其在有无噪声下的表现。
- 干净数据下准确率分别为88%（ResNet）和86%（ViT），噪声下ViT更鲁棒。
- 研究对无人机视觉系统有参考价值，适合关注事件相机应用的开发者。
本研究考察了卷积神经网络（CNN）与视觉变换器（Vision Transformer）在事件相机上的性能表现。事件相机捕捉场景变化，适用于无人机和自动驾驶等动态环境。实验使用在GEN1事件数据集上微调的ResNet34与ViT B16模型，在标准条件与模拟噪声下进行评估。在干净的GEN1数据集上，ResNet34与ViT B16分别达到88%和86%的分类准确率，其中ResNet34略优。然而，尽管预训练数据量较小，ViT B16在噪声环境下表现出更强的鲁棒性。该研究虽聚焦地面车辆分类，但方法与发现对无人机空中目标识别及航空事件相机视觉系统具有重要应用潜力。
原文摘要 · Abstract (English)
This study investigates the performance of the two most relevant computer vision deep learning architectures, Convolutional Neural Network and Vision Transformer, for event-based cameras. These cameras capture scene changes, unlike traditional frame-based cameras with capture static images, and are particularly suited for dynamic environments such as UAVs and autonomous vehicles. The deep learning models studied in this work are ResNet34 and ViT B16, fine-tuned on the GEN1 event-based dataset. The research evaluates and compares these models under both standard conditions and in the presence of simulated noise. Initial evaluations on the clean GEN1 dataset reveal that ResNet34 and ViT B16 achieve accuracies of 88% and 86%, respectively, with ResNet34 showing a slight advantage in classification accuracy. However, the ViT B16 model demonstrates notable robustness, particularly given its pre-training on a smaller dataset. Although this study focuses on ground-based vehicle classification, the methodologies and findings hold significant promise for adaptation to UAV contexts, including aerial object classification and event-based vision systems for aviation-related tasks.
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