# Artificial Intelligence-Enhanced Colorimetric Assessment of Self-Cleaning Road Marking Points
###### Abstract
This interaction leads to the photooxidation of these contaminants and contributes to a self-cleaning effect through photocatalytic properties [12; 14; 15; 16; 17]. This capability has been extensively studied for a range of substrates, including asphalt pavements, mortars, glasses, and architectural paints [14; 15; 16; 18]. [PERSON] et al. [15] evaluated the self-cleaning characteristics of photocatalytic asphalt mixtures functionalized with nano-TiO\({}_{2}\) and micro-ZnO and confirmed their capability to photodegrade Rhodamine B up to 92%. [PERSON] et al. [16] also functionalized asphalt mixtures with nano-TiO\({}_{2}\) and assessed their self-cleaning property by monitoring soot degradation via digital image processing. The results revealed that the soot coverage of the functionalized asphalt pavement decreased from 65.3% to 7.2%. [PERSON] et al. [19] evaluated the self-cleaning properties of glass slides coated with TiO\({}_{2}\) and ZnO. The assessment was performed by monitoring soot degradation via digital image processing, and the results revealed degradation rates of over 90%. Considering self-cleaning properties in paints and coatings for architectural applications, [PERSON] et al. [20] evaluated the effectiveness of protective coatings for external thermal insulation containing TiO\({}_{2}\) and ZnO in degrading Methylene Blue, Rhodamine B, and a  
Figure 1: Application of RM system. commercial dye. The assessment was conducted using colorimetry (CIELAB color space). The results demonstrated discoloration rates of up to 97% after exposure to solar light. [PERSON] et al. [21] studied resin paints and silicate paints functionalized with TiO\({}_{2}\). The results indicated that the degradation rates of Methylene Blue and Rhodamine B, analyzed via diffuse reflectance, led to the nearly complete discoloration of the dyes after just 30 min of UV light exposure. Despite the studies on self-cleaning properties in other substrates and certain types of paints, there is a notable absence of research pinpointing this capability in RM paints [20; 21; 22; 23]. In imparting self-cleaning properties, it is important that we can quantify self-cleaning. In this context, techniques like UV-Vis spectrometry, colorimetry, digital image processing, and visual analysis are valuable for assessing the efficiency of self-cleaning in different substrates, since surface changes translate to color changes. These methodologies offer precise and reliable means to evaluate the effectiveness of self-cleaning technologies and pave the way for advancements in surface engineering and material science [16; 19; 20; 21; 24; 25; 26; 27]. However, regarding these techniques, visual analysis is not accurate if not performed in a controlled environment, and both UV-Vis spectrometry and colorimetry require relatively high costs and complex operations, necessitating specific and expensive equipment. Integrating artificial intelligence (AI) into advanced analytical techniques can offer a promising approach to assessing the self-cleaning efficiency by enhancing the accuracy, speed, and depth of analysis while also reducing the costs for the test. Several studies from different scientific areas have utilized AI to improve colorimetric analysis; for example, [PERSON] et al. [29] combined AI and colorimetry to predict properties of heat-treated wood through artificial neural networks (ANNs). Together, colorimetry and AI were able to predict the physical and mechanical properties of wood. [PERSON] et al. [30] integrated AI into colorimetry for enhanced sensitivity, accuracy, resolution, and anti-illuminating capability in urine glucose detection. The results revealed a high (87.6%) correlation with the standard blood glucose test method. [PERSON] et al. [31] utilized machine vision and AI algorithms for color recognition in food to assess cooking status identification. The results revealed that the judgment time of each image can be performed in less than 0.2 s, and the false judgment rate can be reduced to less than 3%. [PERSON] et al.