摘要：【中文】本发明公开一种自然场景图像文本检测方法、存储介质及终端设备，方法包括步骤：采用深度卷积神经网络模型对待检测图像进行特征提取处理，得到四个阶段的基本特征图；对所述四个阶段的基本特征图进行融合处理，得到三个阶段的深度融合特征图；采用改进的Inception模块对所述三个阶段的深度融合特征图进行聚合操作，得到文本区域概率预测特征图和文本区域位置预测特征图；对所述文本区域概率预测特征图和所述文本区域位置预测特征图进行算法处理，得到自然场景图像中文本的位置。本发明利用多阶段的特征图进行融合，将深度卷积神经网络早期阶段的图像特征信息用于最终的特征图聚合，可有效提高自然场景图像文本检测的准确率。 【EN】The invention discloses a natural scene image text detection method, a storage medium and a terminal device, wherein the method comprises the following steps: performing feature extraction processing on an image to be detected by adopting a deep convolutional neural network model to obtain basic feature maps of four stages; performing fusion processing on the basic feature maps of the four stages to obtain depth fusion feature maps of the three stages; adopting an improved inclusion module to perform aggregation operation on the depth fusion characteristic maps in the three stages to obtain a text region probability prediction characteristic map and a text region position prediction characteristic map; and carrying out algorithm processing on the text region probability prediction characteristic map and the text region position prediction characteristic map to obtain the position of the text in the natural scene image. The method utilizes the multi-stage feature maps for fusion, uses the image feature information of the early stage of the deep convolutional neural network for final feature map aggregation, and can effectively improve the accuracy of natural scene image text detection.