Binarization method of instrument image with uneven illumination and ghosting

The image needs to be binarized during the identification process of the high-voltage meter. However, the phenomenon of uneven illumination and digital ghosting often appears in the instrument images, so that it is difficult to binarize the instrument images with traditional method. Therefore, a binarization method based on convolutional neural network was proposed to binarize the instrument images with digital ghosting under complex illumination. The data sets used in the network were the instrument images in real environment. Firstly, the dimensionality reduction was used to extract features of the input images, and then the foreground of images was reconstructed by deconvolution. Finally, the binary images were output by the network. Comparing the designed network with the traditional binarization method, the experimental results show that the binary images of the proposed network are clear and have no ghosting. The average IoU is 95.12, which is most similar to the sample label images. Therefore, the method can effectively solve the problem of binarization of instrument images with ghosting under complex environment.