Power Equipment Nameplate Text Detection Based on Improved Multiscale Feature Fusion Network
Zhaochen Wang, Xiuxia Tian · 2023
Power nameplate information is essential for managing and maintaining power equipment. In response to issues such as low contrast and high text density in text detection for power nameplates. To address these issues, we propose a text detection algorithm for power nameplates based on the DBIR network (Differential Binarization with Inception-ResNet v2), which integrates multi-scale feature fusion and a Fusion Attention Module (FAM). The DBIR network can learn features of different scales and use the FAM module to fuse deep and shallow features, thus capturing more spatial location information of the text and improving text localization accuracy. Given the current absence of publicly available power nameplate datasets, we propose a hybrid dataset that combines power nameplates with natural scenes. This approach effectively simulates power scenarios and enhances the generalization capability of the model. We evaluated the model on several datasets, including the power nameplate dataset, and obtained promising results. Specifically, the DBIR network achieved a detection accuracy of 87.1%, a recall of 83.2%, and an F-measure of 85.1%.