基于改进EAST算法的电气设备铭牌文字检测OA

In response to the problems of poor detection performance and inaccurate text bounding box detection in the text detection task of electrical equipment nameplates,an improved algorithm based on the deep learning algorithm EAST has been proposed,focusing on optimizing the EAST algorithm's poor detection performance for long texts and small target omissions.ResNet50 residual network is used to replace VGG algorithm as the backbone network for feature extraction of EAST algorithm.At the same time,pyramid attention module and void convolution are introduced to enhance the detail information and correlation between the feature maps of each layer of the image,and expand the receptive field of the feature map,and address the defects of EAST algorithm.In addition,Dice and Focal losses are used to establish the loss function of the improved algorithm to optimize the training process of the improved model.The results show that the improved algorithm has higher recall and accuracy than the traditional EAST algorithm on ICDAR 2015 and the self-established electrical equipment nameplate dataset,with an average improvement of about 7.8%in recall and 4.3%in accuracy,and an overall performance improvement of about 6.3%.