Development of an Analog Gauge Reading Solution Based on Computer Vision and Deep Learning for an IoT Application

Abstract

In many industries, analog gauges are monitored manually, thus posing problems, especially in large facilities where gauges are often placed in hard-to-access or dangerous locations. This work proposes a solution based on a microcontroller (ESP32-CAM) and a camera (OV2640 with a 65° FOV lent) to capture a gauge image and send it to a local computer where it is processed, and the results are presented in a dashboard accessible through the web. This was achieved by first applying a Convolutional Neural Network (CNN) to detect the gauge with the CenterNet HourGlass104 model. After locating the dial, it is segmented using the circle Hough transform, followed by a polar transformation to determine the pointer angle using the pixel projection. In the end, the indicating value is determined using the angle method. The dataset used was composed of 204 gauge images split into train and test sets using a 70:30 ratio. Due to the small size of the dataset, a diverse set of data augmentations were applied to obtain high accuracy and a well-generalized gauge detection model. Additionally, the experimental results demonstrated adequate robustness and accuracy for industrial environments achieving an average relative error of 0.95%.

Keywords

• computer vision
• gauges
• object detection
• CNN
• image processing
• IoT