## 1 Introduction  
The quality of car tires directly impacts the safety and comfort of the vehicle. Therefore, tire marking point detection is crucial prior to the release of automotive tires from the factory. Downstream companies that use these tires require rapid and high-precision inspection of a significant number of tire marking points for efficient storage. Through enterprise research, it has been observed that the current practice predominantly relies on manual inspection, which is time-consuming and labor-intensive. Companies aspire to replace manual inspection with machine vision technology to achieve automated detection, offering a more efficient and accurate solution [1; 2]. As early as the 1990s, domestic and foreign scholars conducted research related to the identification of tire marking points. In 1999, Bridgestone of Japan improved their rotating marking point identification system by integrating a marking point marking device and an identification device, which greatly reduced the installation space of the device and allowed the identification of the marking points immediately after they were marked on the tire [3]. The Identity CONTROL TMI 8303.1 closed-loop control system developed by Micro epsilon, Germany, is used for online identification of tire marking points and can identify the color, tire and location of tire marking points. The IRIS_M, developed by SICK in Germany, uses three-dimensional images to locate the tire and two-dimensional images to identify the shape and color of the points [4]. In [5], a support vector machine approach was used to achieve shape recognition and color recognition of tire identification points. In [6], color recognition and shape recognition are trained for using convolutional neural network SGD. In [7], the recognition area recognition is achieved by a template matching method. The above methods also represent most of the recognition methods for automotive tire marking point features. The advantages and shortcomings of the methods in the literature and the methods used in this paper are shown in Table 1.  
Based on enterprise research and literature review, it is evident that most current methods for automotive tire marking point recognition rely on traditional image processing or manual recognition. However, these approaches suffer from low efficiency and accuracy in recognition. Additionally, there is limited utilization of deep learning, a powerful image feature recognition method, in automotive tire marking point recognition. To address this issue and improve the speed, accuracy, recognition diversity, and reliability of tire marking point recognition, this paper proposes a deep-learning-based method for small target recognition in automotive tire marking points. The proposed method demonstrates high accuracy in recognizing marking points in both tire industrial production environments and outdoor natural environments.  
The article is structured as follows:  
1. Introduction. The research background of tire marking points and its development trend is presented through company research and literature summary.
2. Materials and methods. It mainly introduces the overall scheme of tire marking point recognition, the way of data collection composition, and image pre-processing to solve the influence of light, weather, and other factors.
3. Tire marking point identification network structure. An improved YOLOv5s algorithm is proposed for the problem of tire marking point recognition: (1) Adding an attention mechanism (2) Loss function (3) Small target prediction head
4. Model Training and Testing. To verify whether the improved algorithm is better than the original algorithm and to cerify the rationality of the improvement through ablation experiments.
5. Conclusion. To summarize the whole text and draw conclusions.