# Artificial Intelligence-Enhanced Colorimetric Assessment of Self-Cleaning Road Marking Points
## 4 Conclusions  
This study focused on evaluating the self-cleaning capability achievement of RM paints after the incorporation of nano-TiO\({}_{2}\) nanoparticles. For this purpose, as visual analysis techniques (in a controlled environment), spectroscopy and AI-enhanced colorimetry techniques were employed and compared. The following conclusions can be drawn:  
* Visual analysis showed that, after 48 h of light irradiation, samples with 2% and 3% of nano-TiO\({}_{2}\) exhibited greater discoloration (self-cleaning) than the reference samples or those with lower rates. * The CIELAB color coordinates exhibited a strong linear correlation between the spectrophotometric and AI-assisted colorimetric methods, particularly for a* and b*, with R\({}^{2}\) values of 0.98717 and 0.92296, respectively. The L* coordinate also demonstrated a positive correlation between the methods, with an R\({}^{2}\) value of 0.77798, although this correlation was lower compared to a* and b*. * Colorimetric analysis revealed higher values of photocatalytic efficiency (up to 82%) and color variation (up to 35.76) for the samples with 3% of nano-TiO\({}_{2}\) after 48 h of light irradiation. * The R\({}^{2}\) for PEs versus PEai was 0.99018, and the R\({}^{2}\) for \(\Delta\)Es versus \(\Delta\)Eai was 0.97825. For both parameters, the values obtained by AI-enhanced colorimetry were slightly higher. In this regard, it can be inferred that the incorporation of nano-TiO\({}_{2}\) into a water-based acrylic RM paint conferred self-cleaning properties. It was also concluded that colorimetry is a suitable technique for evaluating photocatalytic efficiency and that AI-enhanced colorimetric analysis is an economical and reliable alternative for this evaluation, as it does not require expensive equipment such as spectrophotometers or extensive programming knowledge for image processing. Thus, AI-enhanced colorimetry, explored in this work, becomes a valuable technique with numerous practical applications. In industry, it can be used for quality control; in the medical field, for diagnostic tests and monitoring of skin diseases; in civil engineering, to assess the aging and degradation of construction materials; and in road engineering, to quantify the self-cleaning properties of various elements beyond RM, such as pavements. Future work on self-cleaning capabilities includes using pollutant models similar to traffic-related pollution (soot), conducting extended duration testing, and developing new methods for immobilizing semiconductors, such as successive spray layers of nanoparticle dispersions during RM paint application. Additionally, it is crucial to evaluate the aging properties and durability of the RM system (luminance and retroreflectance) after functionalization. Moreover, future studies should also consider the potential environmental impact of using nano-TiO\({}_{2}\), assessing its potential toxicity, bioavailability, and mitigation strategies to minimize adverse effects. Such assessments are essential to ensure that the practical applications of self-cleaning technologies are both effective and environmentally sustainable. Regarding the AI-enhanced colorimetry method for evaluating self-cleaning, used in this study, it is valuable to conduct a calibration study of the photobox compared to spectrophotometer measurements, with particular focus on adjusting the photobox lighting conditions to improve the correlation of the L* coordinate. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14219718/s1](https://www.mdpi.com/article/10.3390/app14219718/s1)]([https://www.mdpi.com/article/10.3390/app14219718/s1](https://www.mdpi.com/article/10.3390/app14219718/s1)), Table S1. CIELAB color coordinates via spectrophotometer. Table S2. CIELAB color coordinates via AI-enhanced colorimetry. Table S3. Colorimetric parameters via AUTenhanced colorimetry. Table S4. Colorimetric parameters via AUTenhanced colorimetry. tory Advanced Production and Intelligent Systems ARISE under reference LA/P/0112/2020.