Traffic signboards are vital for road safety and intelligent transportation systems, enabling navigation and autonomous driving. Yet, recognizing traffic signs at night remains underexplored due to the scarcity of realistic public datasets capturing low-light degradations and distractor classes. Existing benchmarks are predominantly daytime and do not reflect challenges such as headlight glare, motion blur, sensor noise, and vandalized or ambiguous signage. To address these gaps, we introduce INTSD, a large-scale nighttime traffic sign dataset collected across diverse regions of India. INTSD contains street-level images spanning 41 traffic signboard classes, multiple distractor categories, and varied lighting and weather conditions. The dataset is designed to support both detection and fine-grained classification under realistic nighttime scenarios. To benchmark INTSD for nighttime sign recognition, we conduct extensive evaluations using state-of-the-art detection and classification models under standardized protocols. Additionally, we present LENS-Net, a strong baseline that integrates adaptive illumination-aware detection with multimodal semantic reasoning for robust nighttime sign classification. Experiments and ablations demonstrate the challenges posed by INTSD and establish competitive baselines for future research. The dataset and code for LENS-Net is publicly available for research.

翻译：交通标志对于道路安全和智能交通系统至关重要，是实现导航和自动驾驶的关键。然而，由于缺乏能够捕捉低光照退化及干扰类别的真实公开数据集，夜间交通标志识别仍是一个未被充分探索的领域。现有基准数据集主要为白天场景，无法反映诸如前照灯眩光、运动模糊、传感器噪声以及被破坏或模糊标志等挑战。为弥补这些不足，我们提出了INTSD，一个覆盖印度多个地区的大规模夜间交通标志数据集。INTSD包含涵盖41个交通标志类别、多种干扰类别以及不同光照和天气条件的街景图像，旨在支持真实夜间场景下的检测与细粒度分类。为在夜间标志识别任务上对INTSD进行基准测试，我们采用最先进的检测与分类模型，在标准化协议下进行了广泛评估。此外，我们还提出了LENS-Net，这是一个强大的基线模型，它通过整合自适应光照感知检测与多模态语义推理，实现了鲁棒的夜间标志分类。实验与消融研究揭示了INTSD带来的挑战，并为未来研究建立了竞争性的基线。该数据集及LENS-Net的代码已公开供研究使用。