NTS-YOLO

NTS-YOLO：a nocturnal traffic sign detection method based on improved YOLOv5In this paper, a nighttime traffic sign recognition method "NTS-YOLO" is proposed, which consists of three main parts. Firstly, this paper adopts the unsupervised nighttime image enhancement technique proposed by Ye-Young Kim et al. Secondly, the Convolutional Block Attention Module (CBAM) attentional mechanism is introduced on the basis of the YOLOv5 network structure, and lastly, the Optimal Transmission Allocation (OTA) loss function is used to optimize the model's performance in the target detection task. With this approach, the accuracy of predicting the bounding box can be effectively optimized so that the model can predict the location of the target and the bounding box more accurately, thus improving the robustness and stability of the model in the target detection task.Other datasIn this paper, 599 nighttime images from the CCTSDB2021 dataset are referenced, of which 80% of the images (479 images) are used as the training set and 20% of the images (120 images) are used as the validation set. In view of the relatively small number of road sign types at night, 9170 daytime road scene images from the TT100K dataset are also referenced to increase the diversity of the data, which are divided into a training set (7208 images) and a validation set (1962 images) at a ratio of 8:2.Links to other publicly accessible locations of the data:CCTSDB2021：GitHub - csust7zhangjm/CCTSDB2021TT100K：http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/data.zipEnvironmentThe experimental environment consists of a high-performance computer configured with an Intel Core i7 processor, 32GB RAM, and an NVIDIA GeForce RTX 4060 graphics card. PyTorch 2.0.1 was chosen as the main deep learning framework, and CUDA technology was utilized to accelerate model training and inference to ensure computational efficiency and data processing power during the experiment.

NTS-YOLO：一种基于改进YOLOv5的夜间交通标志检测方法。本文提出了一种命名为“NTS-YOLO”的夜间交通标志检测方法，其主要由三大模块构成。其一，采用Ye-Young Kim等人提出的无监督夜间图像增强技术；其二，在YOLOv5网络架构基础上引入卷积块注意力模块（Convolutional Block Attention Module, CBAM）的注意力机制；其三，使用最优传输分配（Optimal Transmission Allocation, OTA）损失函数优化模型在目标检测任务中的表现。通过上述方案，可有效优化边界框的预测精度，使模型能够更精准地预测目标位置与边界框，进而提升模型在目标检测任务中的鲁棒性与稳定性。
数据集说明 本文引用了CCTSDB2021数据集的599张夜间图像，其中80%（共计479张）用作训练集，剩余20%（共计120张）作为验证集。鉴于夜间交通标志的类别相对有限，本文还引入了来自TT100K数据集的9170张日间道路场景图像以扩充数据多样性，该部分数据同样按照8:2的比例划分为训练集（7208张）与验证集（1962张）。
数据集公开获取地址如下：
CCTSDB2021：GitHub - csust7zhangjm/CCTSDB2021
TT100K：http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/data.zip
实验环境 本次实验搭载高性能计算机，其配置为Intel Core i7处理器、32GB运行内存以及NVIDIA GeForce RTX 4060显卡。实验选用PyTorch 2.0.1作为核心深度学习框架，并借助CUDA技术加速模型训练与推理流程，以保障实验过程中的计算效率与数据处理能力。