Research on Traffic Sign Recognition Based on Lightweight Multi-Scale Spatial Attention

With the rapid development of driverless technology, traffic sign recognition, as a key link in the intelligent transportation system, has attracted much attention. However, traditional traffic sign recognition methods have problems such as long training time, high model complexity and low accuracy, which are difficult to meet the needs of practical applications. To this end, this paper designs a lightweight traffic sign recognition model for this problem, which is characterized by the introduction of a new attention mechanism to achieve effective capture of key image features. The model shows satisfactory performance after being tested on the Chinese Traffic Sign Database (TSRD) dataset, achieving an optimal recognition accuracy of 99.83% with only 0.05 MB of parametric quantities (Params). The effectiveness of our proposed method is demonstrated through comparative experiments. In addition, this paper is not only in proposing an effective lightweight model, but also in providing new ideas and guidance for the research and practice in the field of traffic sign recognition, which has important theoretical and practical significance.