Abstract: Aiming at the characteristics of natural street text such as skewed angle, curved shape and variable length, an attention mechanism-based natural street text detection method is proposed to improve the overall network detection performance by taking advantage of the attention mechanism and weighting the fusion of features extracted from the backbone network. Firstly, to address the problem of feature information loss in the lateral connection of the feature pyramid network (FPN), we introduce the Attention Feature Fusion Module (AFFM) to improve the feature fusion method of simple direct summation in the original FPN by calculating the fusion weights of high and low-dimensional features, so as to reduce the text information loss in the process of FPN feature fusion and thus enhance the feature detection performance of the network. This reduces the loss of text information in the process of FPN feature fusion, and enhances the feature extraction capability of the network. Secondly, a Subspace Attention Module (SAM) is introduced for text features in feature maps of different scales, by dividing the multi-scale fused feature map into several subspace feature maps by channels, and learning the text feature weights in each subspace separately, so that the fused feature map contains more text features of different scales, thus The fused feature maps contain more text features at different scales, thus enhancing the characterization ability of the fused feature maps for text instances, and thus improving the detection effect of the network. The model is evaluated on the publicly available dataset Total-Text, and the experimental results show that the algorithm improves the accuracy, recall and F-value by 0.5%, 0.4% and 0.4%, respectively, compared with the current fast and efficient DBNet.