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.