# Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning
## 1 Introduction
Firstly, the trained YOLOv5 [19] was used to obtain dashboards. Then, the improved Deeplabv3 [20] was used to segment pointers and numbers to realize the pointer meter reading. [PERSON] et al. [21] used YOLOv5s to locate and identify the digits of the meter. The effectiveness of the proposed automatic digital position identification method for digital meters was verified via practical experimental cases. Although the above-mentioned methods can obtain gauge representation, none of them take into account image blurring. Therefore, the accuracy is low when recognizing blurred images. Most of the existing inspection robots adopt the mode of fixed-point inspection. The robot moves to a fixed position and stays there to complete image acquisition and detection, and then goes to the next designated inspection point. Although this approach can ensure the stability of detection, it greatly increased the inspection time. In order to reduce the inspection time and improve the inspection efficiency, the inspection strategy of \"walk-and-inspect\" was proposed in this paper, which means that the robot inspects the equipment during its movement, and the robot does not need to stop during the image acquisition and inspection. The inspection method can save a lot of time, but the image acquisition during the robot's movement will cause image motion blur. In order to ensure that the inspection robot can complete the inspection task better, it is necessary to detect and restore the blurred image. Image blur detection methods are usually divided into spatial-domain-based image blur detection and frequency-domain-based image blur detection. Methods such as the Tenengrad gradient method [22], Laplacian gradient method, energy gradient method and variance method [23] are often used for image blur detection. The advantages of these types of method are simplicity and quick speed, but the disadvantage is that the threshold value is difficult to determine due to the susceptibility to illumination. In summary, the image blur detection method based on fast Fourier transform in the frequency domain has better stability. In order to guarantee the quality of images, it is necessary to recover imageswith motion blur. Traditional image motion blur recovery algorithms such as the Wiener filtering method [24], edge function method [25] and PSF method [26] have complicated blur kernel estimation processes and the deblurring effect depends largely on the priori assumptions of the image; so, it is difficult to be extended to other types of blurs. Compared to the traditional image motion blur recovery algorithms, the deep-learning-based image motion blur recovery methods achieve motion blur recovery in different scenes by sensing and analyzing image blur information. Deep-learning-based image blur recovery methods had better generalization and robustness. In particular, the deep learning method based on the adversarial neural network cannot only preserve the texture features of the original image, but also has a good effect on the contour recovery of the image. For the image blurring issue caused during motion detection, an image blur recovery method based on an improved deblurring algorithm is suggested. The method can overcome the interference of ambient light changes and achieve the highly robust recovery of motion-blurred images. In addition, for the recognition of LED digital meter readings commonly used in power inspection tasks, traditional detection algorithms have problems such as poor robustness and a high false detection rate. In view of the above situation, this paper adopts the \"coarse to fine\" detection strategy combined with deep learning technology. Firstly, the Polygon-YOLOv5 [27] model was used to detect the LED digital area and correct the LED digital area with perspective transformation. Then, the YOLOv5 model was used for the precise positioning of digits line by line. Finally, the CRNN model was used to recognize the numbers and complete the recognition task of the LED digital meter reading.