###### Abstract
Then, the least-squares and the central projection methods were used to detect the pointer and scale marks and determine the gauge value. This central projection method performs a projection from the feature points to the known gauge center, which results in projecting values associated with an angle. The angles that correspond to the largest number of projection points represent the pointer or even the scale marks. Furthermore, region growing has also been applied to segment the pointer [14], but as is the case for the segmentation of the gauges, the performance of this method is shown to be influenced by the lighting conditions and contrast between the pointer and the dial. For the more difficult task of detecting scale, [PERSON] et al. [15], in 2017, used the K-means clustering method to recognize the tick mark on the scale of an automobile dashboard based on attributes such as area, contour length, and the distance between the centroid of tick marks and pointer rotation center, but this is susceptible to noise. Two years later,  
Figure 1: Flowchart of conventional approaches to automatic gauge reading. [PERSON] et al. [10] expanded this idea by using parametric distributions to recognize the pointer and scale marks. The parameters were the mass of the objects (number of pixels), the distance to the gauge center, the orientation inaccuracy, and the rotated bounding box ratio. Moreover, [PERSON] et al. [3] presented a method to detect the scale marks and pointer by unwinding the circular gauge, which is achieved by applying the polar coordinates transformation. By using the technique of column projection of the pixels, they detected a bigger spike that represented the pointer and small spikes that were the scale marks. This method was made more robust by [PERSON] et al. [7] by using color correction and perspective transformation to obtain the front view of the image taken from an arbitrary camera angle. [PERSON] et al. [4] also used the polar coordinates system with an improved central projection to detect the circular scale region. The final step to automate the reading of analog gauges is determining the gauge value. There are two main techniques: the angle method and the distance method. The angle method determines the value of the gauge by the deflection between the zero-scale mark and the pointer. This technique is simple to use but cannot be applied to non-linear scales. The distance method uses the distance between the nearest scale mark and the pointer to determine the gauge value. This method can be applied to non-linear scales but requires the definition of all the scale marks. In the last few years, the fast development of deep neural networks has led to the research and development of systems capable of reading gauges using this technology. For example, [PERSON] et al. [16] used a Faster R-CNN model to localize gauges in an image captured by an inspection robot; then, if needed, the camera was adjusted according to the detection box. After this, they used more conventional methods, such as the Hough transform, to read the final value. More recently, end-to-end approaches based on CNNs have been proposed, such as by [PERSON] et al. [17], who described a novel method that generated synthetic images by separating the dial and pointer of the gauge image. The pointer is rotated to combine with the dial, creating a synthetic image labeled with the reading value to train the CNN model. Furthermore, [PERSON] et al. [18] proposed a method by first using a Mask-RCNN algorithm to classify the gauge and segment the pointer to later use the line fitting algorithm called Principal Component Analysis (PCA) and read the gauge value. In general, end-to-end approaches are promising but are still limited due to the difficulty of generalization. In this article, a method was proposed based on traditional computer vision techniques complemented with new neural network approaches to read circular gauges even in challenging conditions.