Road Traffic Sign Recognition Algorithm Based on Cascade Attention-Modulation Fusion Mechanism

Road traffic signs can improve the pressure of environmental traffic, and the real-time accurate recognition of traffic signs is conducive to the promotion and development of intelligent vehicles. However, in various complex application scenarios such as tilted and deformed traffic signs, there still have the following problems: First, the feature selection of the existing traffic recognition model only considers its network layer information and fails to retain the salient feature information with strong discriminatory power effectively, and the feature enhancement is easy to introduce background noise. Second, the existing traffic sign recognition models are difficult to deploy on mobile devices and systems, resulting in weak learning capability of feature representation in deep learning backbone network, and low robustness of the high-level feature fusion performance. Therefore, we propose a cascade attention mechanism, which can associate a series of attention units using a cascade approach, and design a cascade attention feature enhancement module, which can effectively improve the feature selection and feature enhancement performance in the traffic sign recognition process. Then, we design a lightweight deep learning model and propose modal fusion, a high-level feature-guided feature refinement mechanism, and a mutual attention enhancement module. In addition, the interrelationship between traffic signs and deep modal features is given, which can effectively enhance the feature representation learning ability of the lightweight deep learning model and significantly improve the efficiency and robustness of the model. The experimental results show that our proposed method achieves the most excellent recognition results on several traffic sign datasets.