提升夜间行车检测精度，实现实时低光照目标检测。
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver Assistance Systems
- 采用增强、小波特征提取与自适应融合三模块设计。
- 在多个数据集上达到当前最佳夜间检测性能。
- 适合车载系统部署，兼顾速度与准确率。
目标检测是高级驾驶辅助系统（ADAS）环境感知的核心。然而，现有方法依赖RGB相机，在低光照条件下因图像质量差导致性能显著下降。为此，本文提出WTEFNet，一种专为低光照场景设计的实时目标检测框架，具备对主流检测器的良好兼容性。该框架包含三个核心模块：低光照增强（LLE）模块用于提升暗区亮度并抑制过曝区域；基于小波的特征提取（WFE）模块通过多级离散小波变换分离高频与低频成分，实现有效降噪并保留结构特征；自适应融合检测（AFFD）模块融合语义与光照特征，提升检测鲁棒性。为支持训练与评估，本文构建了GSN数据集，涵盖晴天与雨夜场景的精细标注。在BDD100K、SHIFT、nuScenes及GSN上的大量实验表明，WTEFNet在低光照条件下达到当前最优精度。此外，在嵌入式平台NVIDIA Jetson AGX Orin上的部署验证了其适用于实时ADAS应用。
原文摘要 · Abstract (English)
Object detection is a cornerstone of environmental perception in advanced driver assistance systems(ADAS). However, most existing methods rely on RGB cameras, which suffer from significant performance degradation under low-light conditions due to poor image quality. To address this challenge, we proposes WTEFNet, a real-time object detection framework specifically designed for low-light scenarios, with strong adaptability to mainstream detectors. WTEFNet comprises three core modules: a Low-Light Enhancement (LLE) module, a Wavelet-based Feature Extraction (WFE) module, and an Adaptive Fusion Detection (AFFD) module. The LLE enhances dark regions while suppressing overexposed areas; the WFE applies multi-level discrete wavelet transforms to isolate high- and low-frequency components, enabling effective denoising and structural feature retention; the AFFD fuses semantic and illumination features for robust detection. To support training and evaluation, we introduce GSN, a manually annotated dataset covering both clear and rainy night-time scenes. Extensive experiments on BDD100K, SHIFT, nuScenes, and GSN demonstrate that WTEFNet achieves state-of-the-art accuracy under low-light conditions. Furthermore, deployment on a embedded platform (NVIDIA Jetson AGX Orin) confirms the framework's suitability for real-time ADAS applications.
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