关键词: 计算机视觉, 农资标签, 语义分割, 文本检测, 文本识别

Abstract: The text on the packaging of agricultural materials contains the registration certificate number, active ingredient content, production license number, product standard number and other producerelated information, which not only provides an important basis for farmers to buy agricultural materials, but also helps agricultural materials supervision agencies to find problems affecting the safety of agricultural materials, as well as greatly helps the identification of agricultural materials for export. Based on the data set of agricultural materials packaging images, a text detection model of agricultural materials labels based on attention mechanism was proposed. The model uses Swin-Transformer as the backbone network, uses FPN to extract text features, designs Twin Feature Fusion Module (TFFM) to integrate local features and global features, and uses scaling expansion algorithm to generate text borders in the prediction stage. The experimental results of the model on the selfbuilt agricultural materials packaging image data set show that the accuracy, recall rate and F value of the model are 91.4%, 87.3% and 89.3%, respectively, which are superior to the mainstream methods and have certain advantages for agricultural materials packaging image text detection task.

Key words: computer vision, agricultural materials labels, semantic segmentation, text detection, text recognition