An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Abstract

Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region’s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera’s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification (“presence/absence” detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating.

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

“…Ali et al [19], Ali et al [20], Hamishebahar et al [21], and Intisar et al [22] studied DL-based approaches to crack detection. Chu et al [23], Qureshi et al [24], and Ranyal et al [25] reviewed the general SOTA in the AVI of pavements and roads, while Kim et al [26] focused more specifically on the detection of potholes. A wider scope is covered by Zhou et al [27] and Hassani et al [28], as they consider structural health monitoring as a whole, not just cracks or pavements and roads.…”

Estimating Pavement Condition by Leveraging Crowdsourced Data

“…Pavement video or image data are collected by cameras or drones. With the advancement of image processing techniques, scholars can identify pavement cracks, ruts, and roughness from images captured by cameras [1,15,16] or aerial images recorded by Unmanned Aerial Vehicles (UAVs) [17][18][19] or Google Maps [20,21]. Comparing cameras mounted on vehicles, UAVs offer a broader perspective and can cover larger areas quickly, yet they cannot capture detailed characteristics of pavement surfaces.…”

An Intelligent Detection and Classification Model Based on Computer Vision for Pavement Cracks in Complicated Scenarios

“…Over the years, three-dimensional (3D) methods utilizing laser scanning, ground penetrating radar (GPR), and unmanned aerial vehicles (UAVs) have been a hot spot for detecting 3D pavement characteristics like volume and depth. PaveVision3D, Pavemetrics, and RICOH are typical automated detection systems that apply 3D techniques [12,13]. For example, PaveVision3D can conduct a complete lane width distress detection survey at 1 mm resolution at a speed up to 100 KM/h [14].…”