# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 2 Related Work
Data for over 1100 signs from Ascension Parish were used in the prediction experiments. The study demonstrated the superior performance of neural networks against regression models in terms of the \(R^{2}\) metric. The study recommended considering several explanatory variables in the analysis in order to improve the prediction accuracy. Accurate prediction of sign retro-reflection is vital for the optimization of maintenance programs and sign a replacement. [PERSON]' et al. used logistic regression to predict sign retro-reflectivity using data collected for 21,467 traffic signs along 30 state roads in the Republic of Croatia [14]. Three types of retro-reflective sheeting were considered. The authors developed a binary logistic regression model to evaluate whether or not signs meet the prescribed retro-reflection values. The proposed model could accurately predict the retro-reflectivity values for signs meeting the minimum retro-reflection standard. However, prediction results for signs not meeting the minimal prescribed retro-reflectivity values were not satisfactory. In an attempt to model the degradation of retro0 reflectivity over time, [PERSON] et al. experimented with linear, logarithmic, polynomial, power, and exponential models [15]. They found that the linear model was the best model to predict the expected retro-reflectivity and they had unique model for each sheeting and color combination. However, the strength of the relationship between age (the only feature used) and retro-reflectivity (\(R^{2}\)) was weak (less than 0.5) which indicate that their models was achieving poor accuracy. [PERSON] and [PERSON] conducted a detailed review of long-term sign deterioration studies carried out by state agencies in the US [16]. It was reported that, out of all the predictors, age had the most significant contribution to deterioration of sign sheeting. Similarly, it was also found that signs facing South deteriorate at a much faster rate than any other direction. [PERSON] and [PERSON] developed a photometric model to establish retro-reflectivity for overhead guide signs using minimum luminance required for sign legibility [17]. The elderly population was specifically emphasized in developing the retro-reflectivity limits. After establishing minimum retro-reflectivity limits, the model was used to investigate the effect of factors such as distance to sign, headlight intensity, and vehicle speed on sign retro-reflection. Minimum retro-reflectivity limits were recommended for overhead guide signs, overhead mast-arm mounted, and post mounted street name signs for different sign sheeting and color. The minimum retro-reflectivity values recommended by this study were developed at an entrance angle of \(-4.0\) degrees and observation angle of 0.2 degrees and are thus not applicable to other measurement conditions. Furthermore, the study considered only two sheeting colors (white and green), which reduces its applicability to other regions having different sign sheeting types and colors. Another study investigated the influence of frost and dew on traffic sign retro-reflectivity and noted an average decrease of 79% and 60%, respectively, in the retro-reflectivity values [18]. Engineering grade (Type-I) sheeting was observed to be severely affected as compared to high intensity (Type-III) sheeting. This study collected sign retro-reflectivity data for only 130 in-service signs during a period of one year (2001-2002). Consideration of a greater sample size over extended service lives may lead to more useful conclusions for practitioners. Furthermore, sign readings were obtained at only one observation angle (0.2\({}^{\circ}\)) and entrance angle (0.4\({}^{\circ}\)). [PERSON] examined the effect of factors such as dirt deposit, drizzle, precipitation, and dew on sign surfaces, which may cause a reduction in sign retro-reflectivity [19]. Study results revealed that precipitation on sign surfaces significantly impair sign retro-reflective properties, decreasing it by over 76% in most cases.