# Traffic Sign Detection Based on the Improved YOLOv5
## 1 Introduction  
With the development of intelligent technology, the rapid detection and recognition of traffic signs has become a hot issue for researchers at this stage. Traffic sign detection plays an important role in intelligent driving; after collecting traffic sign information, an intelligent driving system can make the car automatically plan the path and adjust the speed during the driving process, so as to avoid traffic accidents [1; 2; 3]. Traffic sign detection is an important part of smart driving which lays the foundation for the analysis and decision-making of environmental perception [4; 5]. Accurately detecting traffic signs in complex road environments has become a challenging endeavor. Within the realm of smart driving, traffic sign detection is classified as a vision-based target detection task. This task demands not only high accuracy but also the utilization of limited computational resources, all while maintaining a swift processing rate. As such, it presents a formidable challenge in the development of robust models [6; 7]. Traffic sign detection is used to identify traffic signs' locations from video images and detect them, giving them a specific classification [8]. These methods basically use visual messages, for instance, the shape and color of traffic signs, which may lead to poor detection when objects similar to real traffic signs are present in a scene. Although traditional methods can detect traffic signs, the process is tedious, and the ability to extract features is poor, which is not sufficient tomeet the requirements of end-to-end intelligent driving. Therefore, with the continuous advancement of artificial intelligence, convolutional neural networks are widely employed in the field of object recognition. Currently, some scholars for traffic sign detection tasks have proposed relevant convolutional neural network frameworks [9]. [PERSON] et al. proposed an extraction method for regions of interest which depends on neurons to indicate target information such as path attitude and direction to efficiently obtain traffic sign information from different angles and directions [10]. [PERSON] et al. proposed a compact model design, and a squeezing and excitation model was proposed to raise the capability of salient features by learning correlations between two channels [11]. However, direct global processing of information in the channel easily disregards this information. [PERSON] et al. proposed a GIoU loss function based on IoU [12]. Although GIoU achieves a better focus on the detection accuracy of nonoverlapping regions than IoU, the single-frame image detection takes a long time. [PERSON] et al. proposed a detection network based on pyramidal multiscale fusion, with a significant improvement in accuracy [13]. However, due to the increase in the number of network parameters, the network is unable to address the performance imbalance between detection accuracy and detection speed. Although this algorithm effectively enhances the precision of object detection, its real-time performance is compromised by the complexity of the network. In 2015, you only look once (YOLO) was widely utilized in the area of intelligent driving because of its high-speed detection feature, which encapsulates all calculations in the network model and enables YOLO to complete a target detection task at a substantially faster rate. However, its detection accuracy is still unsatisfactory [14]. In the area of traffic sign recognition, [PERSON] et al. proposed various optimization strategies based on YOLOv3, which encompassed network pruning, scale prediction branching, and loss function improvement, to address the challenges posed by small traffic sign recognition [15]. In 2020, a one-stage network with both recognition accuracy and speed, YOLOv5, was proposed [16]. YOLOv5 is one-stage target detection algorithm that is mainly made up of an input network, backbone network, neck layer, and output network.