## 4 Development of a Decision-Making System  
Following the accurate detection and classification of objects within the digital image, the subsequent phase of this research entailed the development of a decision-making system. The primary objective of this system is to facilitate the categorization of these objects, specifically horizontal road signs, into two distinct groups: signs necessitating repainting and signs not requiring any repainting (due to sufficient visibility). Segments of the images corresponding to one of the nine predefined horizontal road signs were extracted based on the data acquired from the detection and classification processes. These segments were treated as individual elements and underwent further transformations. The initial step involved establishing criteria for determining when a given sign is adequately visible and when its visibility falls below the required threshold. Figure 2 provides examples of signs that are adequately visible and those that are partially not visible. Both categories of objects underwent various transformations within the realm of digital image analysis. It is worth noting that not all techniques yielded significant insights; therefore, only the most intriguing observation will be presented. The most intriguing results were obtained from a series of transformations as follows: Firstly, the image was converted to grayscale. Subsequently, the contrast of this image was increased. Next, its histogram was computed. Before the transformations, the histograms for both well-visible objects and those requiring repainting exhibited very similar trends. However, after the aforementioned transformations, a specific difference in the histogram function was observed. The histogram in the range of maximum saturation values (close to 255) for well-visible objects displayed high values, while objects requiring repainting showed a declining trend in this range. Figure 5 depicts a sample set of graphs for a properly visible horizontal road sign. Figure 5a represents the image with the object, Figure 5b shows the histogram plot of that image, and Figure 5c presents the histogram plot for the image after a series of transformations were applied. Figure 6 presents a similar set of graphs for a poorly visible horizontal road sign. Figure 6a represents the image with the object, Figure 6b shows the histogram plot of that image, and Figure 6c presents the histogram plot for the image after a series of transformations were applied. It is evident that increasing the contrast enhances the saturation of bright colors in the image for properly visible objects. However, performing the same operation on images with incomplete objects increases the saturation only to a limited extent. This is an effect resulting  
Figure 5: An exemplary set comprising an image and plots for a correctly visible horizontal road sign. (**a**) Fragment of the original image with the object. (**b**) Histogram plot of the image from (**a**) transformed into grayscale. (**c**) The histogram plot of the image from (**a**) after a series of transformations. Figure 6: An exemplary set comprising an image and plots for a poorly visible horizontal road sign. (**a**) Fragment of the original image with the object. (**b**) Histogram plot of the image from (**a**) transformed into grayscale. (**c**) The histogram plot of the image from (**a**) after a series of transformations. from the deficiency of white paint (where increasing the contrast brightens gray, albeit to a lesser extent than light gray/almost white). However, when certain parts of an object, such as its edges, are not visible, the object occupies fewer pixels. Consequently, the ratio of object pixels to background pixels is lower than in images featuring fully visible objects. To address this, a series of three operations were conducted on all the analyzed image fragments: conversion to grayscale, contrast enhancement, and histogram calculation. Establishing a threshold value for the decision-making system was imperative.