Learning Under Low Illumination: A Dataset and Algorithm for Traffic Sign Recognition

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.