## 2 Literature Review
### Dataset  
The traffic barrier inventory was collected from 2016 to 2017 as part of the WYDOT traffic barrier inventory project no. HPR1217 [18]. In this period over 1.3 million linear feet of traffic was measured along with approximately 4176 miles of roadway. Also, the study incorporated 7700 images, which were taken during the field survey. The images were taken by Trimble GeoExplorer 6000 series. The objective of that field analysis was to measure various roadway and traffic barriers' geometric features like barriers' height, length, offset, and road slope rate. The collected data included 55% of all non-interstate roads of Wyoming [18]. After filtering the dataset to include only relevant images, more than 2000 very high-quality images were included in the analysis. The images were in 25,952 \(\times\) 1944 pixels, and with horizontal/vertical dots per inch (DPI) of 192. Some of images used for model training, as an example, are presented in Figure 1. Although most of the images are self-explanatory, a description should be given for hybrid barriers. The bottom two images, images g and h in Figure 1, are related to hybrid barriers. Hybrid barriers are a combination of guardrail and box beam barriers being implemented when a guardrail barrier transfer to a box beam due to being located at the bridge. Those barriers would be located on a step of a concrete to assure enough height for preventing vehicles for any possible override over the bridge. Images e and f are related to concrete barriers but they were taken from a bad position. Figure 1: \(Cont.\)The original image sizes were very large and even small frequency of those high-quality images would be very expensive in terms of time, resources, and storage for a model to be trained. Thus, all images were resized to 150 \(\times\) 150 for training, validating and testing dataset. This resizing should not be much of a concern regarding the methodological approach due to the following:  
* Most part of the background images present information unrelated to the barriers, e.g., sky, grass, and thus would be not beneficent to the training. * CNN by itself would pick up important information through filters. For instance, barriers consists of simple geometric characteristics, vertical lines would be picked by 3 \(\times\) 3 filter of 1 0 \(-\)1 and horizontal line by 0 0 0. Thus, it can be 1 0 \(-\)1 and \(-\)1 \(-\)1 assumed using original or resizing the Images would not make much of a difference,
* From practical point of view, it would take a tremendous time for the models to be trained if large images were used. * The objective was to compare across various methods and as we used similar size across the methods, the size would be of no issue. The Figure 2 presents some of the resized imaged in Figure 1 for a comparison. Figure 1: Some of the images used in this study, from top rows down: Box beam (**a**,**b**), cable barrier (**c**,**d**), concrete barrier (**e**,**f**), and a hybrid (**g**,**h**). As the primary objective of this study was to identify various barrier types based on images, images were manually labeled. These images were classified into 4 categories, box beam, cable barriers, hybrid and concrete barriers. The frequencies of these images are presented in Table 1. As noted from Figure 1, although there were instances where it was hard to read the images, but those images were included in the evaluation model. For instances, this could be observed from images d, cable barrier, and f, concrete barrier, in Figure 1, which are not distinguishable easily by an ordinary individual. Out of 316 images for each category, 250, 56, and 10 images were used for training, validation and test dataset, respectively. The percentages of validation, test and training are based on the investigators discretion. Normally 8% of images should be reserved for validation and 82% for training. Training is an important part of our methodological steps. So, we allocate a high number to this category, especially because our data suffer from low number of observations.