YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction

• URL: http://arxiv.org/abs/2503.13883v3
• Date: Sun, 29 Jun 2025 16:45:14 GMT
• Title: YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction
• Authors: Ziyu Lin, Yunfan Wu, Yuhang Ma, Junzhou Chen, Ronghui Zhang, Jiaming Wu, Guodong Yin, Liang Lin,
• Abstract summary: YOLO-LLTS is an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments.<n>YOLO-LLTS introduces three main contributions: the High-Resolution Feature Map for Small Object Detection (HRFM-SOD), the Multi-branch Feature Interaction Attention (MFIA) and the Prior-Guided Feature Enhancement Module (PGFE)<n>Experiments show that YOLO-LLTS achieves state-of-the-art performance, outperforming previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night.
• Score: 45.79993863157494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic sign detection is essential for autonomous driving and Advanced Driver Assistance Systems (ADAS). However, existing methods struggle with low-light conditions due to issues like indistinct small-object features, limited feature interaction, and poor image quality, which degrade detection accuracy and speed. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. YOLO-LLTS introduces three main contributions: the High-Resolution Feature Map for Small Object Detection (HRFM-SOD) module to enhance small-object detection by mitigating feature dilution; the Multi-branch Feature Interaction Attention (MFIA) module to improve information extraction through multi-scale features interaction; and the Prior-Guided Feature Enhancement Module (PGFE) to enhance image quality by addressing noise, low contrast, and blurriness. Additionally, we construct a novel dataset, the Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios. Experiments show that YOLO-LLTS achieves state-of-the-art performance, outperforming 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, 7.5% mAP50 and 9.8% mAP50:95 on GTSDB-night, and superior results on CCTSDB2021. Deployment on edge devices confirms its real-time applicability and effectiveness.

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