Aiming at the problem of nameplate detection and recognition of smart meter communication modules

this paper proposes a cascaded deep learning method based on ResSE-SegNet

which adopts image preprocessing techniques such as Gaussian filtering and adaptive image contrast enhancement

and determines the input through a deep semantic segmentation network.The region where the manufacturer′s name is located in the image is segmented using a deep codec network structure

and an end-to-end convolutional neural network (CNN) model is constructed and trained to identify different manufacturers.Finally

the data set of the communication module image is obtained through the full-dimensional smart meter detection system

and the detection and recognition experiment of the communication module nameplate is carried out.The experimental results show that this method achieves a recall rate of 92.34%

a precision rate of 93.97%

and an F1-Score of 92.14%. The classification accuracy rate for smart meter communication module manufacturers is 98.53%.