## 1 Introduction  
Road infrastructure plays an essential role in any nation's socioeconomic development. In Poland, as in many countries, the importance of road signs--both vertical and horizontal--cannot be overstated. These signs not only guide the myriad of road users but, more importantly, ensure their safety. However, while much emphasis is often placed on vertical signs, the relevance of horizontal road signs is sometimes understated, even though they bear equal significance. Horizontal road signs in Poland, characterized by their light-colored designs, have been strategically chosen to contrast the naturally dark road surfaces, enhancing their visibility. This contrast ensures visibility for road users, especially for signs primarily in white hues. Moreover, the paint applied to these signs contains reflective components, enhancing visibility, particularly in adverse weather conditions and during nighttime. The effectiveness of horizontal road signs hinges on their unobstructed visibility. Obstacles to their visibility can be categorized into two types. The first type arises from obstructions caused by other road users, objects, or adverse weather conditions such as rain or snow. The second reason for reduced visibility is the natural wear and tear of the signs, leading to paint deterioration. Repainting decisions are made by relevant road authorities. In the realm of damage-recovery planning, diverse solutions are employed to optimize each stage of the process [1]. Collaboration among multiple maintenance teams proves invaluable in establishing a sustainable approach to selective maintenance sequence planning [2]. Experienced drivers can intuitively recognize partially obscured signs. However, this demands sustained attention and complicates interpretation, posing an unnecessary safety risk. Severely worn signs become unrecognizable, posing a serious threat. Hence, the concept of developing an integrated system for detecting horizontal road signs and assisting in the decision to repaint them arose. In recent years, technological advancements, particularly in the realm of computer vision and artificial intelligence, have opened new avenues to address challenges that were traditionally tackled manually. Given that road signs are specifically designed for human visual perception, it is logical to consider computer vision as a solution to detect and assess the state of these signs, especially when manual inspections can be resource intensive and less efficient. In this paper, we explore the potential of leveraging state-of-the-art computer vision techniques, backed by robust convolutional neural networks, to detect, analyze, and subsequently decide on the maintenance needs of horizontal road signs in Poland. We aim to bridge the gap between advanced technological solutions and traditional infrastructure challenges. Our research not only contributes a potentially scalable solution to Poland's road safety but also offers insights that could be beneficial for similar challenges in other parts of the world. In the latest research in the field of computer vision analysis, artificial intelligence is successfully used. The convolutional models are selected depending on the purpose and the available dataset. Regression models are used for issues related to value estimation [3,4]. There are many models for detecting objects [5, 6, 7]. Research on the classification of objects is popular [8, 9, 10]. Image segmentation is also widely researched [11, 12, 13, 14]. Some solutions combine image segmentation with the simultaneous classification of found objects. Good examples are popular YOLO models. At the moment, they already exist in many variants: YOLOv1 [15], YOLOv2 [16], YOLOv3 [17], and YOLOv4 [18]. Each subsequent version is better than the previous one, whether in terms of accuracy or speed. The YOLOv4-Tiny model [19, 20, 21] seemed to be the most appropriate for the studies presented in this article. It is a modification of YOLOv4, thanks to which it loses a bit in detection quality but significantly gains in speed.