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English(EN) Towards All-Day Perception for Off-Road Driving: A Large-Scale Multispectral Dataset and Comprehensive Benchmark

新的IRON数据集和IRONet框架推动全天候越野自动驾驶感知能力

研究人员推出了IRON数据集,这是一个大规模的红外和RGB图像集合,专门用于越野自动驾驶感知,尤其是在夜间条件下。该数据集包含超过24,000张标注图像,并支持用于时间自由空间检测的新算法的开发。为了利用该数据集,研究团队还提出了IRONet,一个使用记忆注意力机制来提高图像帧之间一致性的新颖框架,在IRON数据集上取得了最先进的结果,并展示了对其他数据集的泛化能力。 AI

影响 为改进越野自动驾驶系统中的全天候感知能力建立了一个新的基准和数据集。

排序理由 这是一篇介绍新数据集和特定人工智能应用基准的研究论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的IRON数据集和IRONet框架推动全天候越野自动驾驶感知能力

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Shuo Wang, Jilin Mei, Wenfei Guan, Shuai Wang, Yan Xing, Chen Min, Yu Hu ·

    面向越野驾驶的全天候感知:大规模多光谱数据集与综合基准测试

    arXiv:2604.27499v1 Announce Type: new Abstract: Off-road nighttime autonomous driving suffers from unreliable visible-light perception, making infrared modality crucial for accurate freespace detection. However, progress remains limited due to the scarcity of annotated infrared o…

  2. arXiv cs.CV TIER_1 English(EN) · Yu Hu ·

    面向越野驾驶的全天候感知:大规模多光谱数据集与综合基准测试

    Off-road nighttime autonomous driving suffers from unreliable visible-light perception, making infrared modality crucial for accurate freespace detection. However, progress remains limited due to the scarcity of annotated infrared off-road datasets and the inter-frame inconsisten…