## 6 Discussions and Future Directions  
There are approximately 800 miles of interstates, and over 4000 miles of state and U.S. highways in the State of Wyoming [31]. The WYDOT needs to monitor and manage the assets they own. The automated method would help them to manage and maintain those assets in a systemized manner. In addition, it would help them to prioritize the maintenance of their resources in a most efficient way. Assets maintenance and optimization has been conducted for various purposes such as safety, comfort and convenience of road users. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Non-Transfer Learning**} \\ \hline \hline  & \multicolumn{5}{c}{**Predicted**} \\ \hline
**Category** & **Box Beam** & **Cable** & **Concrete** & **Hybrid** & **accuracy** \\ \hline Box beam & 6 & 0 & 0 & 4 & 60\% \\ Cable & 2 & 7 & 1 & 0 & 70\% \\ Concrete & 0 & 0 & 10 & 0 & 100\% \\ Hybrid & 1 & 0 & 0 & 9 & 90\% \\ \hline \hline  & \multicolumn{5}{c}{**Predicted**} \\ \hline \hline
**Category** & **Box Beam** & **Cable** & **Concrete** & **Hybrid** & **Accuracy** \\ \hline Box beam & 10 & 0 & 0 & 0 & 100\% \\ Cable & 0 & 9 & 1 & 0 & 90\% \\ Concrete & 0 & 0 & 10 & 0 & 100\% \\ Hybrid & 0 & 0 & 0 & 10 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion matrix of 4-class of barrier types in test data, non-transfer learning and transfer learning. Assets maintenance needs an extensive and up-to-date condition data. However, securing the information requires an extensive amount of funding. For instance, collecting information related to the traffic barriers alone cost WYDOT more than half a million dollars. It makes sense that the data collection to be automated in the future to reduce cost. This study was conducted by using limited barrier images to determine if a trained algorithm could distinguish across various barrier types. The main objective of this study was to evaluate image recognition related to the roadside object detection, specifically roadside barriers. Due to the low frequency of images in this study, various transfer learning techniques first were implemented to address the lack of images. A pre-trained weight related to inception v3, VGG 19, and Densenet 121 models, being trained on imagenet, were considered in this study. In those models, two blocks were added, and the majority of pre-trained layers were left frozen while the last top few layers were left to be unfrozen. In summary, only few last layers and a dense layer for prediction were left to be trained on traffic barriers dataset. In addition, to have a base model for comparison, a simple model with few convolutional layers was considered and trained to be used for barrier images identification. The transfer learning of VGG 19 was able to reach an accuracy of 90% by just misclassifying a single image. That is despite the use of only 250 images for training the algorithm. The results showed that although the VGG 19 outperformed all the models, non-transfer learning perform promising compared with the other two transfer learning techniques. The high performance was despite resizing the images to much smaller dimensions. The resizing was due to the future application of the method on the roadway: The thin clients with limited processing capabilities would send the images to server with a limited bandwidth, and the data would be stored in the database. So it is impractical to transmit the images descriptions to the server with huge considered size. Also another objective was to test if we could reach an acceptable accuracy before moving to the next phase of the project. One of the shortcomings of the trained model for non-transfer learning was inability to have a good prediction between hybrid barriers (box beam + guardrail), and box beam. This issue might be addressed by having more images and constructing a deeper CNN.