Abstract
Road pavement conditions have significant impacts on safety, travel times, costs, and environmental effects. It is the responsibility of road agencies to ensure these conditions are kept in an acceptable state. To this end, agencies are tasked with implementing pavement management systems (PMSs) which effectively allocate resources towards maintenance and rehabilitation. These systems, however, require accurate data. Currently, most agencies rely on manual distress surveys and as a result, there is significant research into quick and low-cost pavement distress identification methods. Recent proposals have included the use of structure-from-motion techniques based on datasets from unmanned aerial vehicles (UAVs) and cameras, producing accurate 3D models and associated point clouds. The challenge with these datasets is then identifying and describing distresses. This paper focuses on utilizing images of pavement distresses in the city of Palermo, Italy produced by mobile phone cameras. The work aims at assessing the accuracy of using mobile phones for these surveys and also identifying strategies to segment generated 3D imagery by considering the use of algorithms for 3D Image segmentation to detect shapes from point clouds to enable measurement of physical parameters and severity assessment. Case studies are considered for pavement distresses defined by the measurement of the area affected such as different types of cracking and depressions. The use of mobile phones and the identification of these patterns on the 3D models provide further steps towards low-cost data acquisition and analysis for a PMS.
Highlights
Agencies are tasked with implementing pavement management systems (PMSs) which effectively allocate resources towards maintenance and rehabilitation
Recent proposals have included the use of structure-from-motion techniques based on datasets from unmanned aerial vehicles (UAVs) and cameras, producing accurate 3D models and associated point clouds
This paper focuses on utilizing images of pavement distresses in the city of Palermo, Italy produced by mobile phone cameras
Summary
Road networks are key drivers towards the economic viability of a country They provide movement for users, goods and services as well as providing access to social benefits for commuters [1]. To achieve suitable maintenance strategies, a pavement management system is commonly applied by road agencies. The pavement management system (PMS) is seen as the most common and effective system for crafting maintenance strategies and it can be characterized as one that optimizes road management to achieve the most effective use of financial resources given the needs of the road system [4]. Given the already strained road agency budgets, this leads to many authorities not being able to implement an effective PMS as in many instances road condition surveys are manually carried out [7]. As a result of this, there has been a significant amount of research carried out to obtain new but accurate and low-cost methods of acquiring road condition data, data on pavement distresses that are present within a network [8,9]
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