Automated Pavement Distress Detection, Classification and Measurement: A Review

Abstract

Road surface distress is an unavoidable situation due to age, vehicles overloading, temperature changes, etc. In the beginning, pavement maintenance actions took only place after having too much pavement damage, which leads to costly corrective actions. Therefore, scheduled road surface inspections can extend service life while guaranteeing users security and comfort. Traditional manual and visual inspections don't meet the nowadays criteria, in addition to a relatively high time volume consumption. Smart City pavement management preventive approach requires accurate and scalable data to deduce significant indicators and plan efficient maintenance programs. However, the quality of data depends on sensors used and conditions during scanning. Many studies focused on different sensors, Machine Learning algorithms and Deep Neural Networks tried to find a sustainable solution. Besides all these studies, pavement distress measurement stills a challenge in Smarts Cities because distress detection is not enough to decide on maintenance actions required. Damages localization, dimensions and future development should be highly detected on real-time. This paper summarizes the state-of-the-art methods and technologies used in recent years in pavement distress detection, classification and measurement. The aim is to evaluate current methods and highlight their limitations, to lay out the blueprint for future researches. PMS (Pavement Management System) in Smarts Cities requires an automated pavement distress monitoring and maintenance with high accuracy for large road networks.

Enhancing Pavement Sustainability: Prediction of the Pavement Condition Index in Arid Urban Climates Using the International Roughness Index

“…Field data were collected by field inspectors who visually examined the pavement surface conditions considering distress type, severity, and extent. The current study utilized a paver system for distress data collection, which included specific guidelines regarding distress classification, and the quantification of severity and extent in certain measurement units (area or length or number) based on the type of distress [34]. Detailed descriptions of the 19 distress types implemented in this paver system, along with the guidelines, are reported by Shahin, M.Y.…”

Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions

“…Artificial neural networks (ANN) [41] are ML models consisting of interconnected processing nodes and have been influential in pavement analysis [42]. Deep learning models, have proven efficacy in pavement-related tasks such as distress detection [22,[43][44][45]. Additionally, many road management tasks involving visual inputs are likely to significantly benefit from the capabilities of neural networks [21,[45][46][47][48].…”

“…A major challenge is obtaining high-quality data for model training. Pavement performance data is often collected manually or semi-automated, leading to time-consuming, costly, and inaccurate results [20][21][22]. Then, to create ML models that work well for a broad range of situations, it is crucial to standardize the collection, handling, storage, and accessibility of the data.…”

An automatic image processing based on Hough transform algorithm for pavement crack detection and classification

“…Having a rapid, accurate and cost-effective tool for detecting pavement surface cracks constitutes a crucial component of any robust pavement maintenance management system (Hosseini and Smadi, 2021;Justo-Silva and Ferreira, 2019). In turn, the system (to support crack identification) requires a triumvirate of major processes (Benmhahe and Chentoufi, 2021) viz. 1) data collection -using either manual or automated methods to represent the pavement's current state (Elghaish et al, 2021); 2) data analysis -to create indices to represent pavement quality (Justo-Silva and Ferreira, 2019); and 3) maintenance planning -to develop an optimal road network repair plan (Peraka and Biligiri, 2020).…”

“…Extant literature reveals that automated pavement condition assessment techniques are gaining momentum due to their reasonable cost of image acquisition equipment and the continuous improvements of IP techniques (Coenen and Golroo, 2017). However, major problems doggedly confront contemporary automated crack detection solutions, and these include inhomogeneous and diverse crack patterns, illumination, different types of noise, false positives and false negatives classifications and multiple types of cracks in one image (Benmhahe and Chentoufi, 2021;Fujita et al, 2017;Peraka and Biligiri, 2020). Thus, further scientific investigation is required to enhance the efficacy of pavement cracks classification methods.…”