# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
###### Abstract  
Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management. p 2021 11 3152 2021 18 February 2021 27 March 2021 1  
Roadway pavements are subject to a wide range of types of deterioration, such as vehicular friction, since they directly carry traffic loads. Additionally, recent years have witnessed increased traffic demands due to social development, which further imposes challenges on pavement maintenance. Common forms of distress range from cracking, rutting, and potholes to graffiti. Among them, deterioration of pavement markings is one of the most critical issues that road managers are concerned about. All types of markings are faced with wear and tear problems even though long-life materials have been introduced. Worn markings typically manifest as being blurred and in worst case scenarios, missing. Figure 1 presents a few examples of pavement markings that have suffered from wear and tear issues. As a result, pavement markings in poor condition might fail to convey useful information to drivers, imposing potential dangers to traffic. To maintain road safety and efficiency for citizen's daily commutes, road agencies and traffic organizers have been dedicated to making sure that pavement markings remain in a satisfactory condition and keep regulating the traffic. They are responsible for regular inspections of the entire road assets and accordingly, proper maintenance decision-makings, e.g., repainting for pavement markings. Other factors, such as environmental costs and benefits, will also be considered during the decision-making process [2]. However, current approaches require (trained) inspectors to tour on site, manually identify roadway markings in poor condition and document such condition data, which involves considerable amount of human effort. Additionally, due to the lack of human resources, some local authorities and/or governments fail to fulfil adequate inspection practices. An alternative method to manual inspections is to deploy unmanned aerial vehicles (UAVs) and computer vision (CV) technology. In particular, computer vision has gained heated attention from both academia and industry in recent years due to its capability to replace tedious manual work, automate image processing, and obtain reliable information out of visual data. Among the numerous disciplines it has been applied to, its deployment in the architecture, engineering and construction (AEC) community covers the entire lifecycle of civil assets.