# Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
## 1 Introduction
### The Need for Automated Detection Systems for Condition Monitoring  
Given the importance of detecting, classifying and analyzing distresses, there have been numerous attempts exploring different methodologies for detecting pavement distresses [5]. The methods researched can generally be grouped in terms of the equipment utilized, which are namely laser technologies, camera and imagery tools, in situ sensors, radar and other less researched methods such as pressure-based, acoustic and vibration methods. Of these technologies, the two most researched and utilized in industry are laser and image-based methods with the laser-based ones generally having a higher accuracy but also carrying significantly higher costs. The in situ sensors have also shown value in their use given that sensors can be deployed within the pavement allowing for remote condition monitoring [6; 7]. Sensors can be deployed in various forms allowing sensor information, such as strain and deflection, to be collected for interpretation using structural health monitoring algorithms. Many of these applications involve wired sensors but these have difficulties for deployment given their fixed nature and they can also be costly [8]. Others have looked at the use of wireless smart sensors [9]. There are merits to these systems but there are still significant studies needed to understand the data from these sources and verifying the life expectancy of the sensors. Laser systems have been used to develop systems such as the Laser Crack Measurement System (LCMS) [10], Laser Road Imaging System (LRIS) [11] and other vehicular systems developed in Australia [12] by combining lasers systems with imagery as well. The laser profiling systems have been developed based on this and are seen to be components in several commercial road systems such as ARAN, ROMDAS and Dynatest [5] and they offer accurate assessments of the state of the road but at a higher cost. With respect to the image-based systems, there has been a lot of research done on the use of images to detect and analyze specific distresses with systems being developed around Digital cameras, Line-scan cameras and 3D Imaging with methods achieving suitable accuracies in replicating distress patterns and classifying them [13]. These systems offer a cheaper alternative to carrying out distress surveys as opposed to laser-based systems. Recent work has also included research on low-cost assessments of 3D models for pavement distress analysis wherein the accuracy of models created using the techniques of photogrammetry and 3D modelling have been validated [14]. Whilst this and similar methods show accuracy in replicating the distress, it is also important to quickly pinpoint where the distresses are occurring. Therefore, as a precursor to more sophisticated methods, it is useful to have a low-cost system able to determine the specific locations of the distresses which would then be followed by detailed assessments. Together these would form a low-cost tool ready to be used by road authorities. To quickly perform a hotspot analysis of a road network would be very useful for any road authority. To this end, the use of Artificial Intelligence and deep learning technologies have gained traction in this automated process and towards this type of pavement distress hotspot analysis. This paper aims at utilizing deep learning techniques namely the use of object detection within imagery collected, using low-cost smartphones, to gain a rapid assessment of the condition of a road network based on standardized distress techniques and severity determinations and to demonstrate the ability to use this process to continuously monitor the condition of the pavement structure. The research explores the requirements needed for a deep learning model for this type of work which can be applied in different environments and regions.