MIAF-Net: A Multi-Information Attention Fusion Network for Field Traffic Sign Detection

In complex field environments, traffic sign detection faces many challenges, such as the effects of light variations, occlusion, and sensor resolution, which can lead to a decrease in detection accuracy. To cope with these problems, a multi-information attention fusion traffic sign detection network MIAF-Net is proposed. First, a backbone network with linear transformations was designed to improve the efficiency and accuracy of feature extraction. Second, an attention balance feature pyramid network was designed to enhance the correlation between foreground features and surrounding semantics, refine and balance semantic features, and improve the expressive ability of feature maps by fusing and learning multiscale information. Finally, a detection head with multiscale information fusion is designed to provide different features for category prediction and boundary regression, increasing the reliability of traffic sign detection and classification. In the experimental part, three traffic sign datasets (TT100K, CCTSDB, and DFG) were used to fully evaluate MIAF-Net and compare it with existing state-of-the-art traffic sign detection methods, and the results show that MIAF-Net exhibits a very superior performance in the traffic sign detection task. In addition, in the edge device deployment experiments, MIAF-Net demonstrates its real-time performance and low memory access, which shows that the proposed method is not only superior in accuracy but also has good deployment utility.