Open-Vocabulary Prior Guided Mamba Framework for Closed-Set Nighttime Object Detection

Nighttime object detection is essential for road, urban, and traffic perception, where detectors are typically required to recognize a predefined set of categories under a closed-set protocol. However, severe illumination degradation, noise amplification, and weak textures make visual evidence unreliable, leading to semantic ambiguity and missed detections. To address this issue, we propose Night-Mamba-World, a knowledge-guided framework that incorporates open-vocabulary priors into closed-set nighttime object detection, thereby effectively complementing degraded visual features with language-aligned semantic knowledge. Night-Mamba-World unifies language-aligned open-vocabulary priors, frequency adaptation, illumination-guided state-space aggregation, and inference-time prompt calibration within a single detection framework. Night-Mamba-World consists of three modules. Visual Fourier Prompt Tuning (VFPT) mitigates illumination-style mismatch while preserving phase-based structural information. Retinex-Guided Mamba-PAN (RG-Mamba-PAN) enhances long-range context aggregation in dark and weak texture regions through illumination-guided state-space modeling. ModPrompt further improves robustness to varying nighttime conditions via online prompt calibration during inference. Experiments on ExDark, LLVIP, and BDD100 K demonstrate that Night-Mamba-World achieves consistent improvements across diverse nighttime scenarios, reaching 0.831$mAP_{50}$on ExDark, 0.929$mAP_{50}$on LLVIP, and 0.545$mAP_{50}$on BDD100 K. It also maintains deployment-oriented efficiency with 49.564 M parameters, 91.451 G FLOPs, 13.972 ms latency, and 71.57 FPS.