# NTS-YOLO: A Notcurnal Traffic Sign Detection Method Based on Improved YOLOv5
## 1 Introduction  
With the rapid progress of autonomous driving technology and the increasing maturity of smart connected vehicles, safe and efficient night driving is increasingly dependent on the support of artificial intelligence. However, complex and unstable lighting conditions at night have a huge impact on the visibility and recognition rate of traffic signs. Under the combined effect of streetlights, headlights, and other light sources, it becomes more difficult to recognize traffic signs with small, fuzzy, and occluded targets, especially for automated driving systems that are highly dependent on visual sensors. Therefore, forautomatic driving systems, how to accurately recognize various traffic signs in nighttime environments is undoubtedly a key issue that needs to be continuously studied and solved in this paper. Currently, the automatic detection and recognition of traffic signs is a key application in the field of computer vision and image processing, which involves the automated detection and interpretation of traffic signs in road images and video streams. This technology is capable of recognizing various types of traffic signs, such as speed limits, prohibition signs, and directional signs, thus providing important real-time information and warnings for driving. As a core component of self-driving car research, this technology area has been extensively studied, but in traditional research, the detection of traffic signs mainly relies on color segmentation [1; 2; 3; 4; 5; 6] and shape detection [7; 8; 9; 10] methods, and the traditional methods for the recognition of traffic signs are mainly template matching [11; 12; 13] and manual feature extraction [14; 15; 16; 17; 18]. However, these traditional methods have limitations in detection and recognition accuracy, sensitivity to changes in illumination and the viewing angle, and the ability to handle complex scenes. In recent years, the rapid development of deep learning technology has revolutionized traffic sign detection and recognition, and significant research results and progress have been achieved in the application of traffic sign detection and recognition by virtue of its efficient capability in image recognition and processing. In traffic sign detection, [PERSON] et al. [19] proposed a candidate region-based target detection algorithm, RCNN, which utilizes a deep convolutional neural network to achieve target detection and semantic segmentation but suffers from the problem of repetitive feature extraction and storage and has low computational efficiency; subsequently, [PERSON] et al. [20] proposed a spatial pyramid pooling network (SPP-Net) to improve the processing speed and quality of CNN feature extraction by computing the entire input convolutional feature mapping of the image and extracting features from different candidate regions on the feature map, while using a spatial pyramid pooling layer to eliminate the fixed size constraints of the network, thus improving the processing speed and the quality of CNN feature extraction. However, the training of SPP-Net is multi-stage rather than an end-to-end approach; the proposal of Fast RCNN [21] achieves end-to-end detection trained on shared convolutional features, and its VGG16 network is nine times faster than RCNN and three times faster compared to SPP-Net. Faster RCNN achieves a higher mAP by introducing the RPN [22] and FPN, which further improves the detection accuracy for small targets. In order to obtain a detection bounding box with a more accurate location, [PERSON] et al. proposed the IoU-Net network [23], which uses the network to train the IoU branch and extracts the localization confidence of each bounding box, which improves the accuracy of localization. But the IoU-Net network does not have a strong correlation with the commonly used loss, and it cannot accurately differentiate the alignment of the two objects. For this reason, [PERSON] et al. [24] proposed to use the GIoU loss function as the loss of the bounding box regression branch, which can improve the accuracy by 2% to 14%.