###### 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%. computer vision; gauges; object detection; CNN; image processing; IoT 20223364-580. [[https://doi.org/10.3390/telecom3040032364-580](https://doi.org/10.3390/telecom3040032364-580)]([https://doi.org/10.3390/telecom3040032364-580](https://doi.org/10.3390/telecom3040032364-580)). image, and segmenting it, detecting the pointer, detecting the scale marks, and reading the final value. For gauge segmentation, most of the traditional approaches were based on fitting the gauge shape. The most common method used through the years has been the Hough transform applied to circles. For example, in 2013, [PERSON] et al. [3] applied a local contrast normalization to deal with uneven lighting and the Canny edge detector to execute the final circle detection using the Hough transform. Region growing has also been applied in some research articles [4, 5], taking advantage of the contrast between the white dial and background. This technique can perform poorly if the contrast is low or if there are glares in the dial, which can make the border of the gauge noisy. More recently, in 2019, [PERSON] et al. [6] proposed a technique to detect gauges by first using the Sobel filter to extract edges and then finding the border of the gauge by combining a line fitting algorithm with the Random Sample Consensus (RANSAC) method. With the information on the remaining edge points, they proposed a RANSAC ellipse algorithm to find the most fitted ellipse. This method showed to be especially good with images of gauges captured under angled viewpoints. Straight-line fitting methods were also proposed by many researchers to detect the gauge pointer; these methods include the Hough transform and the least square method. The first method can achieve good results for the dials of gauges that have words and symbols but can be challenging in difficult light conditions [4, 7, 8]. However, the second method, in general, yields better results with less computational power [9, 10, 11]. Moreover, the image subtraction method has also been used to segment the region of the pointer [12, 13]. This method is often performed before the straight-line fitting method to increase the robustness of the pointer detection by eliminating the background interference. This is achieved by subtracting images with roughly the same backgrounds with the pointer in different positions. The resulting image can have a lot of noise and produce unreliable results. In 2008, [PERSON] et al. [9] proposed a noble method to detect the pointer using image subtraction with a dynamic threshold to segment the image. For the scale marks and pointer recognition, the article introduces a technique named region-segmentation to partition the dial image and only process the desired area.