动态驾驶场景下，模型通过数据增强与集成检测实现自适应，夜间识别准确率显著提升。
TTA-DAME: Test-Time Adaptation with Domain Augmentation and Model Ensemble for Dynamic Driving Conditions
- 利用源域数据增强目标域，模拟真实天气变化
- 多检测器集成结合非极大值抑制，提升夜间等极端条件下的性能
- 适合自动驾驶系统在复杂光照环境下实时自适应
测试时自适应（TTA）要求模型在目标域动态变化时仍能保持最优表现，尤其在频繁发生天气变化的真实驾驶场景中。为此，我们提出 TTA-DAME 方法，将源域数据增强引入目标域，并引入领域判别器和专用领域检测器，以缓解从白天到夜晚的剧烈领域偏移。为进一步提升适应能力，训练多个检测器并通过非极大值抑制（NMS）融合其预测结果。实证验证表明，该方法在 SHIFT 基准测试上表现出显著性能提升。
原文摘要 · Abstract (English)
Test-time Adaptation (TTA) poses a challenge, requiring models to dynamically adapt and perform optimally on shifting target domains. This task is particularly emphasized in real-world driving scenes, where weather domain shifts occur frequently. To address such dynamic changes, our proposed method, TTA-DAME, leverages source domain data augmentation into target domains. Additionally, we introduce a domain discriminator and a specialized domain detector to mitigate drastic domain shifts, especially from daytime to nighttime conditions. To further improve adaptability, we train multiple detectors and consolidate their predictions through Non-Maximum Suppression (NMS). Our empirical validation demonstrates the effectiveness of our method, showing significant performance enhancements on the SHIFT Benchmark.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。