WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver-Assistance Systems
Journal:
arXiv
Published Date:
May 29, 2025
Abstract
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.
