ACDC:不利条件数据集与对应对强大的语义驾驶场景感知
IEEE transactions on pattern analysis and machine intelligence
|November 14, 2025
概括
负面条件数据集与对应 (ACDC) 提供了一个大规模的数据集,用于训练和测试在雾,雨,雪和夜间等具有挑战性的天气条件下自动驾驶感知系统.
科学领域:
- 计算机视觉 计算机视觉
- 自主驾驶系统 自主驾驶系统
- 机器学习 机器学习
背景情况:
- 5级驾驶自动化需要先进的视觉感知,能够处理各种环境条件.
- 驾驶时语义感知现有的数据集受到正常条件偏差或小规模的限制.
- 迫切需要全面的数据集,以捕捉不利的视觉场景.
研究的目的:
- 介绍不利条件数据集与对应 (ACDC) 以实现自动驾驶中的强大的视觉感知.
- 在不利条件下促进语义感知模型的培训和评估.
- 能够研究不确定性意识的语义细分.
主要方法:
- 编制了ACDC,一个由8012张图像组成的数据集,其中4006张图像在雾,夜间,雨和雪条件中均分布.
- 为不利条件图像和相应的正常条件图像提供了像素级的全视图注释.
- 包含用于图像内不确定性评估的二进制面具,并支持标准和新型细分任务.
主要成果:
- ACDC对当前监督和无监督的最先进方法提出了重大挑战.
- 经验研究强调了数据集在识别现有方法的弱点方面的实用性.
- 数据集的各种不利条件揭示了语义感知模型中的性能差距.
结论:
- 在恶劣条件下的自动驾驶中,ACDC是促进视觉感知的宝贵资源.
- 该数据集将指导未来的研究和开发更具弹性感知系统.
- ACDC及其基准的公开可用性将加速该领域的进展.
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