YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
Detecting traffic signs effectively under low-light conditions remains a
significant challenge. To address this issue, we propose YOLO-LLTS, an
end-to-end real-time traffic sign detection algorithm specifically designed for
low-light environments. Firstly, we introduce the High-Resolution Feature Map
for Small Object Detection (HRFM-TOD) module to address indistinct small-object
features in low-light scenarios. By leveraging high-resolution feature maps,
HRFM-TOD effectively mitigates the feature dilution problem encountered in
conventional PANet frameworks, thereby enhancing both detection accuracy and
inference speed. Secondly, we develop the Multi-branch Feature Interaction
Attention (MFIA) module, which facilitates deep feature interaction across
multiple receptive fields in both channel and spatial dimensions, significantly
improving the model's information extraction capabilities. Finally, we propose
the Prior-Guided Enhancement Module (PGFE) to tackle common image quality
challenges in low-light environments, such as noise, low contrast, and
blurriness. This module employs prior knowledge to enrich image details and
enhance visibility, substantially boosting detection performance. To support
this research, we construct a novel dataset, the Chinese Nighttime Traffic Sign
Sample Set (CNTSSS), covering diverse nighttime scenarios, including urban,
highway, and rural environments under varying weather conditions. Experimental
evaluations demonstrate that YOLO-LLTS achieves state-of-the-art performance,
outperforming the previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on
TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, and achieving superior
results on the CCTSDB2021 dataset. Moreover, deployment experiments on edge
devices confirm the real-time applicability and effectiveness of our proposed
approach.
