Benchmarking Nighttime Traffic Sign Recognition with Illumination-Adaptive Detection and Semantic Attribute Reasoning

Abstract

Traffic signboards are vital for road safety and intelligent transportation systems. Yet, recognizing traffic signs at night remains underexplored due to the scarcity of real-world 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, designed to support both detection and fine-grained classification under nighttime scenarios. To benchmark INTSD, 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 an end-to-end adaptive illumination-aware detector with a multimodal classifier that fuses vision-language representations with soft semantic attribute reasoning over learnable shape and color embeddings. Experiments demonstrate that models trained exclusively on daytime data fail substantially under real nighttime conditions - a gap that is recovered once INTSD is introduced in training, even when controlling for data volume. These results validate INTSD as a complementary nighttime training resource and establish competitive baselines for future research. The code and dataset are publicly available.