# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 6 Conclusions and Future Work  
This paper was aimed at presenting a deep learning pipeline and framework for the purpose of carrying out a low-cost hotspot analysis of the pavement distresses that are present on an urban road network for the purpose of continually monitoring the health of the pavement structure. The intention of this was to provide road authorities and engineers with a low-cost platform for quickly understanding not only the type of distresses that are present on the network but also provide an idea of the severity of the distresses which can be stored in an asset database for decision making and monitoring purposes. This would, therefore, lead to better planning for interventions to restore the health of the pavement to an acceptable standard which is critical for any city or town. Artificial Neural Networks were developed based on local imagery in the Sicilian region in Italy, to set up a model capable of carrying out this task. The imagery was captured using a low-cost smartphone. For the model, a harmonized severity classification was developed which grouped distresses based on the type of damage and their perceived safety and comfort impact. These classifications allowed the model to produce predictions that adequately can carry out the hot-spot analysis required for the study to enable a rapid assessment of the damages to the road. Future work will focus on how to integrate other types of groupings as well as the surface defects as these distresses as they are important to the overall determination of road conditions. Future work will also aim to establish which distresses are more important to be assessed based on the area under analysis. The models produced showed great accuracy in identifying the distress categories developed and the  
Figure 19: Histogram displaying of sections of the road and the respective percentages distressed. associated severities. An essential assessment of the models was simply their ability to identify the distress type and severity. However, it must be noted that whilst the models created were found to be suitable for the case study, a new model development should be done for a different city or region. Therefore, what is critical is the pipeline needed to create such a model and this is replicable regardless of the scenario as depicted in the study's methodology and by the ease of use of the available opensource platforms and deep learning techniques. Consequently, this process can be replicated in other regions at a similar low-cost. The data collection processes used also can lead to crowd-sourcing platforms that could be developed on cloud-based systems using smartphones in which datasets are generated from commuters along the network and not the road authority itself. Once the model is created it can be deployed for use by authorities for the hot-spot analysis. This can be then be complemented by other low-cost techniques such as 3D Imaging with smartphone and drone imagery to provide detailed quantitative assessments of the areas which are considered in need of intervention. This combination provides a step further for the low-cost automation of the system and allows the authority to receive the data necessary for their PMS optimization schedules. The information obtained through the hotspot analysis is also very useful and this study demonstrated this by applying the information over a test section. The results of this case study showcased how useful the data is, not only for the asset database but also for the purpose of making critical maintenance intervention decisions and creating a viable channel for monitoring the health of the pavement or pavement network being investigated.