# NTS-YOLO: A Notcurnal Traffic Sign Detection Method Based on Improved YOLOv5
## 1 Introduction
[37] introduced an optimization algorithm called ETSR-YOLO, which firstly improved the PANet of YOLOv5s by generating an additional high-resolution feature layer to enhance the multi-scale feature fusion and improve the recognition of small-sized objects; secondly, two improved C3 modules were introduced to suppress the background noise interference and enhance the feature extraction of the network; and finally, in the postprocessing stage, a Wise-IoU (WIoU) function was introduced to improve the learning ability and robustness of the algorithm. [PERSON] et al. [38] proposed a dedicated deep learning model that enhances the recognition of blurred traffic signs by using multi-scale convolutional stacking in the input layer. They analyzed the recognition performance of indication signs, prohibition signs, speed limit signs, and warning signs under different levels of occlusion based on the Chinese Traffic Sign Database. However, existing detection methods face issues such as slow result acquisition and low accuracy. To address these issues, [PERSON] [39] proposed an improved network based on lightweight convolutional neural networks, which improved the training speed of the network and completed traffic sign recognition faster and more accurately. [PERSON] et al. [40] proposed a traffic sign recognition algorithm that combines a CNN and an extreme learning machine (ELM). This method utilizes ResNet50 to extract image features, uses a region proposal network (RPN) to generate proposals, and then classifies them using ELM, followed by regression prediction through a fully connected layer. [PERSON] et al. [41] proposed a traffic sign detection method based on a lightweight multi-scale feature fusion network. [PERSON] et al. [42] proposed a lightweight convolutional neural network recognition method for multi-interference scenarios. This approach enhances images through Gamma correction and histogram equalization, merges MobileNet-V2 and DeepLab-V3+ for segmentation, and finally uses Lw-CNN for the adaptive recognition of traffic signs and markings. However, the above research mainly focused on daytime scenes and research is lacking on nighttime scenes. In summary, there is a relative lack of research on nighttime traffic sign detection and recognition, mainly focusing on daytime conditions. However, the complex and unstable lighting conditions at night result in features that are relied upon for daytime vehicle detection to be ineffective in nighttime environments, especially in long-distance, fuzzy, and partially occluded situations where the traffic sign recognition accuracy is not high. Therefore, this study improved traffic sign recognition methods, especially their performance in nighttime environments. By optimizing and adjusting advanced algorithms such as YOLO, combined with the in-depth analysis and processing of local nighttime traffic data, the system's recognition accuracy and robustness under low-light conditions were improved, thus ensuring safe driving in nighttime environments and providing more reliable technical support for self-driving cars.