# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 2 Related Work
The presence of drizzle and dew were also noted to degrade sign retro-reflection. Similarly, it was noted that the presence of dirt deposits on sign surfaces also had statistical evidence of deteriorating retro-reflectivity. Appropriate sign cleaning resulted in over 60% improvement in retro-reflection levels. [PERSON] and [PERSON] conducted a comprehensive review of factors influencing the night-time visibility of retro-reflective traffic signs [20]. The study reported that predominant factors include vehicle headlight characteristics such as height and color, weather and ambient conditions, angle of illumination, and type of retro-reflective material used. [PERSON] and [PERSON] utilized Random Forest (RF) to predict traffic sign vandalism as the function of several predictor variables such as mount height, size, background color, exposure, land cover, and road type [21]. Chi-square test results showed that there was a significant association between all the sign attributes and vandalism. The authors also provided a ranking of predictor variables on the rate of traffic sign vandalism. A study conducted by [PERSON] et al. (2020) presented a detailed survey on the utilization of sophisticated vehicle-camera systems for the recognition of retro-reflective traffic signs using the Czech Republic as a case study [22]. Field signs' retro measurements were taken by a retro-reflectometer followed by doing the same using modern camera systems. The study identified a number of essential parameters/traffic sign characteristics that significantly impact the performance of traffic sign detection and recognition (TSDR). The fundamental concept of sign retro-reflectivity studies is analogous to pavement marking retro studies. In a recent study, [PERSON] et al. (2021) proposed a scheme to identify significant controllable factors and covariates affecting the retro-reflectivity performance of pavement markings from the perspective of highway safety [23]. The fitted naive-mixed linear model results revealed that any of the following covariates might be used: equivalent single axle load (ESAL), equivalent axle load, or traffic volume. [PERSON] et al. conducted a study for determining the expected service life of a traffic sign with respect to retro-reflection [24]. The authors proposed different linear regression models for each combination of sheating class and color. Their model achieved poor results, with \(R^{2}\) being 0.4436. [PERSON]' et al. predicted traffic signs' functional service life using different regression-based modeling approaches such as linear, logarithmic, and exponential [1]. The proposed methods showed better prediction accuracy compared to previous studies with a coefficient of determination (\(R^{2}\)) value of over 0.5 for all the models. The authors argued that predicting traffic sign degradation based on their reference models can be very useful to practitioners for planning and optimization of the maintenance system. Although \(R_{A}\) prediction performance expressed in terms of \(R^{2}\) were good, low sample size, particularly, for sheating type-III was the main issue. Furthermore, the authors compared the minimum \(R_{A}\) values for new signs and not with in-field service signs, which is more appropriate. [PERSON] et al. used linear and non-linear regression models to predict traffic signs' expected life for 1600 signs surveyed throughout the state of North Carolina in the US [25]. The results of the developed regression models suggested that the average sign replacement interval should be extended to 12 years. Variables such as sign age, amount of shade, and the type of retro-reflective material had significant correlation to the retro-reflectivity degradation of traffic signs. [PERSON] and [PERSON] developed a microscopic simulation model for examining sign replacement strategies using North Carolina (NC) sign data [26]. The authors argued that establishing a reasonable trade-off between sign-cost and conditions is vital for sign replacement practices.