# Improving Tire Specification Character Recognition in the YOLOv5 Network
## 1 Introduction  
Automobile tires are an important part of automobile safety, and their design, manufacture, and use require strict standards and specifications [1]. The tire production process involves a large number of character marks, such as specifications, models, batches, and other information, which are of great significance for the quality control and traceability of tires. In the production of automobile tires, it is necessary to classify the tires according to the size of the specifications and different models. The traditional method of manually recognizing tire characters is time-consuming and inefficient and has a high error rate. Tire character recognition using machine vision is more effective than the traditional recognition method, but it has higher requirements for the recognition environment and harsher conditions. Therefore, it is of great practical significance to study an automatic tire identification method. Currently, many achievements have been made in character recognition based on machine vision systems. [PERSON] et al. [2] proposed a machine vision-based tire rubber surface character recognition method which implements character recognition through character localization, character segmentation, morphological processing, and template matching. [PERSON] et al. [3] proposed a steel plate billet spray marking character recognition system which uses an image sensor to capture images in real-time and the Baidu Paddle-OCR character recognition algorithm to achieve automatic character recognition. [PERSON] et al. [4] proposed a multi-line character recognition method for clutch flywheels. Firstly, the clutch flywheel image is preprocessed and the edge coordinates are extracted. Secondly, the circular ring area is located by using the DBSCAN clustering algorithm, and then character segmentation is performed using the pixel projection method. Finally, LBP (local binary patterns) and support vector machine are used for recognition. [PERSON] et al. [5] proposed a machine tool information acquisition method which uses the projection transformation principle to determine the machine tool panel range, preprocesses the acquired image using filtering methods, and, finally, achieves character recognition through convolutional neural networks. [PERSON] et al. [6] developed a chip character recognition system. Firstly, the character area is obtained by using the grayscale value projection method for character segmentation. Secondly, chip positioning is performed using shape matching, and then character recognition is achieved through BP (back propagation) neural networks. Traditional machine vision methods have higher requirements for image quality and have lower versatility. The recognition effect will also change when the environment changes. With the development of deep learning technology, object detection and recognition have greatly improved. The YOLO (You Only Look Once) algorithm series is comprised of fast and accurate object detection algorithms [7]. Through improvements to YOLOv5's model structure, the introduction of new data augmentation techniques [8], higher detection speed, and better accuracy have been achieved. YOLOv5 technology has also been widely used. [PERSON] et al. [9] proposed a steel stamp character recognition method, based on YOLOv5, which first expanded the dataset through image preprocessing (which was trained with YOLOv5) and then recognized characters using the trained model. However, a large amount of computing resources is required for training and running during training. [PERSON] et al. [10] proposed a vehicle and tank number detection and recognition method based on an improved YOLOv5 network which added attention mechanisms and GBN modules to enhance the feature extraction capabilities and improve detection speed. Adding attention mechanisms improved accuracy but increased the model's computational complexity, leading to overfitting.