In order to solve the problem that the dial deformation affects the reading when the camera shoots the instrument from the overhead and elevation angles in industry, a secondary correction method based on Faster R-CNN is proposed to identify the deformation instrument image. ResNeXt50 is used as the backbone Network of Faster R-CNN, and the Feature layer is generated by combining Feature Pyramid Network (FPN). Moreover, SENet (Squeeze and Excitation Networks) module is integrated to make the meter features more prominent, so it is easy to locate and trim the meter area. The four vertices of the dial are located by Harris corner detection. Using the second correction method, the deformation instrument is reduced to the front Angle instrument. Finally, calculate the indicator number. The experimental results show that the mAP value is increased from 75.51% of the basic model to 94.45% by this method, and the instrument can still get a good reading result under the deformation condition, and the error rate is 0.83%.