## 1 Introduction  
### Problem Description and Motivation  
Road transport plays a vital role in the economic development of a country, connecting communities and businesses while providing access to educational and employment opportunities, as well as healthcare and social services [1]. However, with the fast economic expansion and technological advancements in recent years, the quality of the transportation system has been affected. Ultimately, as road conditions deteriorate, traffic resilience weakens [2]. One of the most significant issues faced by drivers is the presence of potholes on roads. These not only cause damage to vehicles but also pose a risk to driver safety. According to a study by the AAA in 2016, potholes cost U.S. drivers USD 15 billion over the past five years in vehicle repairs, equivalent to nearly USD 3 billion per year [3].  
Potholes can be caused by a variety of environmental factors, such as severe weather, high traffic loads [4], and heavy vehicles, as well as poor construction methods and lack of proper maintenance [5]. These issues are particularly difficult to address as they are often unexpected and require constant monitoring to ensure the road infrastructure [6] is in good condition. To mitigate the risk posed by potholes, a system that can detect and communicate their presence to maintenance services or other drivers could be implemented. This would not only improve driver safety but also assist relevant authorities in determining which potholes pose the greatest risk and need to be repaired as soon as possible [7]. The development of a variety of technologies that enable the collection, processing, and transfer of data in real time between fixed traffic devices, moving vehicles, and data centres has opened up a wide range of new possibilities for Intelligent Transport Systems (ITSs) [8].  
The development of intelligent pothole detection technology is crucial to address issues with road traffic. However, in natural settings, the detection of potholes can be hindered by adverse weather conditions, such as rain, snow, and fog, as presented in [9; 10; 11]. On rainy days, potholes may be hidden under puddles or resemble puddles. Water on the windshield of a car can obstruct the driver's field of view and make it difficult to detect any damage to the road. The reduced visibility induced by the fog increases the likelihood that the car will sustain damage from potholes. Additionally, even in favourable conditions, there is still the possibility of erroneous detection. The results of the pothole detection in good-visibility conditions are displayed in Figure 1. On a sunny day, it is possible to observe instances of false detection, such as the reflection of a pothole on a car hood or a little road patch, denoted in green.  
Pothole detection in real conditions is a complex task that involves several factors, including the type of road surface, the condition of the road, and the traffic volume [12]. One of the main challenges in pothole detection is the ability to accurately identify and locate potholes in real time. Regardless of the approach used, pothole detection in real conditions requires a combination of hardware and software to accurately identify and locate potholes. This includes the use of advanced sensors, sophisticated algorithms, and machine learning techniques to process and analyse the data collected. Additionally, it is important to consider the environmental conditions, such as weather and lighting, which can affect the accuracy of pothole detection [13]. It is expected that with the continued development of sensor technology and machine learning techniques, pothole detection will become increasingly accurate and reliable in the future.