Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning

Abstract

Aiming at solving the issue of blurred images and difficult recognition of digital meters encountered by inspection robots in the inspection process, this paper proposes a deep-learning-based method for blurred image restoration and LED digital identification. Firstly, fast Fourier transform (FFT) is used to perform blur detection on the acquired images. Then, the blurred images are recovered using spatial-attention-improved adversarial neural networks. Finally, the digital meter region is extracted using the polygon-YOLOv5 model and corrected via perspective transformation. The digits in the image are extracted using the YOLOv5s model, and then recognized by the CRNN for digit recognition. It is experimentally verified that the improved adversarial neural network in this paper achieves 26.562 in the PSNR metric and 0.861 in the SSIM metric. The missing rate of the digital meter reading method proposed in the paper is only 1% and the accuracy rate is 98%. The method proposed in this paper effectively overcomes the image blurring problem caused by the detection robot during the detection process. This method solves the problems of inaccurate positioning and low digital recognition accuracy of LED digital meters in complex and changeable environments, and provides a new method for reading digital meters.

Keywords

• YOLOv5s
• DeblurGAN
• blurred reduction
• digital meter identification