# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 5 Discussions
### Case Study Showing an Application of the Model for Monitoring the Health of a Road Network  
Additionally, whilst previous works have been focused on the creation of deep learning models for the purpose of detecting road pavement distresses, it is also critical to have an idea of how the models can be practically applied in the real world and within practical management systems. To this end, this study also created a pipeline to show how the information from the deep learning model can effectively be utilized and integrated within the asset database and subsequently the management system. From the discussions of the study, it was demonstrated that the model developed generates a bounding box for each observed distress within the image as well as a judgement on the severity based on the annotation parameters (a level of 1 or 2 for each distress type). This is coupled with the accuracy of the detection. With this information, a database displaying and storing the location of the distress per image along a road network can be generated and be kept for monitoring comparisons and purposes. For this to be done, each image captured during a survey has to be accounted for, highlighting not only the detection of the distress but also a relationship of this to the length and area of road segment being surveyed. For this to be possible, a pipeline for this to be done is illustrated in Figure 17 below. In the pipeline, the area of the road being surveyed is taken into account to have an approximation of the area that the distress covers and as a result, the total distressed areas along a road segment. Figure 16: Pipeline for hotspot analysis and future integration. Figure 17: Pipeline for applying the model in real-world conditions for health monitoring. This is an approximate calculation based on the length of the road segment which is clearly visualized within an imager during the survey (10 m utilized for the survey and case study). Given this pipeline, an example road section during the survey was utilized as a case study to demonstrate the practicality of carrying out these tasks. A random section of approximately 2.6 km was identified to test the pipeline. The area of the road distressed at each 10 m interval was determined and this was plotted for the full section. This is depicted in Figure 18.  
From this figure, you can visually identify the sections of the analyzed road that are more distressed. For the section under analysis, it appears that between the road chainage of 2000 m to 2600 m the distressed area is greater than the other sections of the road which means that this section could be attributed with a classification of 'critical' health. Therefore, this gives the road practitioner a quick overview of the section under analysis and can allow the agency to pinpoint the area most in need of rehabilitation and maintenance. More critical examinations can be made over this section to determine metric analyses of the distresses and the pipeline utilizing more advanced visual and metric modeling displayed in Figure 16 can be utilized for this. Furthermore, the sections with less distressed areas can be noted within the database and monitored over time. This creates the possibility of monitoring the health of the road network as the survey with the mobile phone can easily be replicated over time creating a timeline of events to monitor. Another use of this information is to create comparisons of different roads along a network with the goal being to determine which roads should be prioritized for interventions. This is also critical for road agencies as it is typical for an agency to be faced with a scenario where there are several roads that have many distressed areas but there are only sufficient funds for rehabilitation to be done on a selected number of the roads. To this end, the data can be utilized to create a histogram showcasing the number of sections with different levels of distress.