Night-Time Traffic Light Recognition Based on Enhancement-Guided Object Detection

Abstract

Traffic light recognition is crucial for autonomous driving. While significant progress has been made in favorable conditions, recognition performance in night-time scenes remains a challenge. One straightforward approach is to apply enhancement methods that improve degraded images prior to object detection. However, since most enhancement methods are tailored for human perception; they may not consistently improve recognition accuracy for machine learning techniques. To address this, we propose an enhancement-guided framework for night-time traffic light recognition, called EG-TLR. EG-TLR consists of a residual denoising module (RDM) and a mixed attention traffic light detection module (MATLDM). The RDM reduces noise in degraded night-time images while preserving essential traffic light features by extracting sparsity information and performing context aggregation. The MATLDM improves feature extraction and recognition performance in complex night-time scenes by incorporating a shadow detection layer (SDL) and a mixed attention module (MAM). Moreover, to address the lack of a dedicated night-time traffic light dataset, we construct the Night-TL dataset utilizing publicly available images. Extensive experiments on Night-TL and LISA datasets demonstrate that EG-TLR achieves an AP50 of 79.83% and an AP50:95 of 36.62%, with an inference speed of 5.9 ms and 16.9 GFLOPs, outperforming other state-of-the-art methods. Furthermore, ablation studies and visualization results validate the effectiveness of our proposed method. The Night-TL dataset can be downloaded from: https://github.com/feiqinaqian/Night-TL-dataset.