Abstract
Fast and accurate object perception in low-light traffic scenes has attracted increasing attention. However, due to severe illumination degradation and the lack of reliable visual cues, existing perception models and methods struggle to quickly adapt to and accurately predict in low-light environments. Moreover, there is the absence of available large-scale benchmark specifically focused on low-light traffic scenes. To bridge this gap, we introduce a physically grounded illumination degradation method tailored to real-world low-light settings and construct Dark-traffic, the largest densely annotated dataset to date for low-light traffic scenes, supporting object detection, instance segmentation, and optical flow estimation. We further propose the Separable Learning Vision Model (SLVM), a biologically inspired framework designed to enhance perception under adverse lighting. SLVM integrates four key components: a light-adaptive pupillary mechanism for illumination-sensitive feature extraction, a feature-level separable learning strategy for efficient representation, task-specific decoupled branches for multi-task separable learning, and a spatial misalignment-aware fusion module for precise multi-feature alignment. Extensive experiments demonstrate that SLVM achieves state-of-the-art performance with reduced computational overhead. Notably, it outperforms RT-DETR by 11.2 percentage points in detection, YOLOv12 by 6.1 percentage points in instance segmentation, and reduces endpoint error (EPE) of baseline by 12.37% on Dark-traffic. On the LIS benchmark, the end-to-end trained SLVM surpasses Swin Transformer+EnlightenGAN and ConvNeXt-T+EnlightenGAN by an average of 11 percentage points across key metrics, and exceeds Mask RCNN (with light enhancement) by 3.1 percentage points. The Dark-traffic dataset and complete code is released at this https URL.
Abstract (translated)
在低光照交通场景中，快速而准确的对象感知吸引了越来越多的关注。然而，由于严重的照明退化和缺乏可靠视觉线索，现有的感知模型和方法难以迅速适应并准确预测在这种环境下的表现。此外，目前还没有专注于低光交通场景的大规模基准数据集可用。为了填补这一空白，我们引入了一种基于物理原理的光照退化方法，并针对现实世界的低光设置构建了Dark-traffic，这是迄今为止最大的密集标注的低光交通场景数据集，支持目标检测、实例分割和光流估计。我们进一步提出了分离学习视觉模型（SLVM），这是一种生物启发式的框架，旨在增强在恶劣照明条件下的感知能力。SLVM整合了四个关键组成部分：一种光照适应性的瞳孔机制，用于提取对光照敏感的特征；一种有效的表示策略——基于特征级别的可分离学习方法；针对特定任务解耦分支，进行多任务可分离学习；以及一种空间错位意识融合模块，实现精确的多特征对齐。广泛的实验表明，SLVM在保持较低计算开销的同时达到了最先进的性能。值得注意的是，在Dark-traffic数据集上，它比RT-DETR的目标检测性能高出11.2个百分点，比YOLOv12的实例分割性能高出6.1个百分点，并将基准线模型的端点误差（EPE）降低了12.37%。在LIS基准测试中，全卷积训练后的SLVM在关键指标上平均超过了Swin Transformer+EnlightenGAN和ConvNeXt-T+EnlightenGAN 11个百分点，比带光照增强功能的Mask RCNN高出3.1个百分点。Dark-traffic数据集及完整代码已在此网址发布。
URL
https://arxiv.org/abs/2509.05012
