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

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\u27s 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\u27s 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

Fast Segmentation of Industrial Quality Pavement Images using Laws Texture Energy Measures and k-Means Clustering

Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture non-uniformities making their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough and expedited health monitoring of roads. In the pavement monitoring area, well known texture descriptors such as gray-level co-occurrence matrices and local binary patterns are often used for surface segmentation and identification. These, despite being the established methods for texture discrimination, are inherently slow. This work evaluates Laws texture energy measures as a viable alternative for pavement images for the first time. k-means clustering is used to partition the feature space, limiting the human subjectivity in the process. Data classification, hence image segmentation, is performed by the k-nearest neighbor method. Laws texture energy masks are shown to perform well with resulting accuracy and precision values of more than 80%. The implementations of the algorithm, in both MATLAB and OpenCV/C++, are extensively compared against the state of the art for execution speed, clearly showing the advantages of the proposed method. Furthermore, the OpenCV based segmentation shows a 100% increase in processing speed when compared to the fastest algorithm available in literature

SMARTI - Sustainable Multi-functional Automated Resilient Transport Infrastructure

The world’s transport network has developed over thousands of years; emerging from the need of allowing more comfortable trips to roman soldiers to the modern smooth roads enabling modern vehicles to travel at high speed and to allow heavy airplanes to take off and land safely. However, in the last two decades the world is changing very fast in terms of population growth, mobility and business trades creating greater traffic volumes and demand for minimal disruption to users, but also challenges, such as climate change and more extreme weather events. At the same time, technology development to allow a more sustainable transport sector continue apace. It is within this environment and in close consultation with key stakeholders, that this consortium developed the vision to achieve the paradigm shift to Sustainable Multifunctional Automated and Resilient Transport Infrastructures. SMARTI ETN is a training-through-research programme that empowered Europe by forming a new generation of multi-disciplinary professionals able to conceive the future of transport infrastructures and this Special Issue is a collection of some of the scientific work carried out within this context. Enjoy the read

Municipal Road Infrastructure Assessment Using Street View Images

Road quality assessment is a crucial part in Municipalities' work to maintain their infrastructure, plan upgrades, and manage their budgets. Properly maintaining this infrastructure relies heavily on consistently monitoring its condition and deterioration over time. This can be a challenge, especially in larger towns and cities where there is a lot of city property to keep an eye on. Municipalities rely on surveyors to keep them up to date on the condition of their infrastructure to prevent this failure before it happens. This is both to prevent injuries and further damage from occurring as a result of infrastructure failure, and since it is can be more cost effective to maintain property rather than have to replace it. Surveying can either be done manually or automatically, but it is not done frequently as it is expensive and also time consuming. Manual surveying can be inaccurate, while a large portion of automatic surveying techniques rely on expensive equipment. To solve this problem, we propose an automated infrastructure assessment method that relies on Street View images for its input and uses various computer vision and pattern recognition methods to generate its assessments. First, we segment the image into 'road' and 'background' regions. We propose a road segmentation algorithm specifically aimed at segmenting roads from street view images. We use Fisher vectors calculated on SIFT descriptors to encode small windows extracted from the main image at multiple scales. Then we classify these patches using an SVM and utilize a Gaussian voting scheme to obtain a segmentation. We additionally utilize a spatial prior to improve this segmentation. Optionally, we improve the segmentation further by making use of a weighted contour map calculated on a shadow-free intrinsic image, and a find an optimal segmentation by utilizing a purity tree. Our algorithm performs well and outputs a good segmentation for further use in road evaluation. We test our method on the KITTI road dataset, and compare it to the state-of-the-art on this dataset, along with a manually annotated subset of Google Street View. After segmenting the road, we describe an algorithm aimed at identifying distressed road regions and pinpointing cracks within them. We predict distressed regions by re-using the computed Fisher vectors and classifying them with a different SVM trained to distinguish between road qualities. We follow this step with a comparison to the weighed contour map within these distressed regions to identify exact crack and defect locations, and use the contour weights to predict the crack severity. Promising results are obtained on our manually annotated dataset, which indicate the viability of using this cost-effective system to perform road quality assessment at a municipal level

Dashcam-Enabled Deep Learning Applications for Airport Runway Pavement Distress Detection

23-8193Pavement distress detection plays a vital role in ensuring the safety and longevity of runway infrastructure. This project presents a comprehensive approach to automate distress detection and geolocation on runway pavement using state-of-the-art deep learning techniques. A Faster R-CNN model is trained to accurately identify and classify various distress types, including longitudinal and transverse cracking, weathering, rutting, and depression. The developed model is deployed on a dataset of high-resolution dashcam images captured along the runway, allowing for real-time detection of distresses. Geolocation techniques are employed to accurately map the distresses onto the runway pavement in real-world coordinates. The system implementation and deployment are discussed, emphasizing the importance of a seamless integration into existing infrastructure. The developed distress detection system offers significant benefits to the Utah Department of Transportation (UDOT) by enabling proactive maintenance planning, optimizing resource allocation, and enhancing runway management capabilities. Future potential for advanced distress analysis, integration with other data sources, and continuous model improvement are also explored. The project showcases the potential of low-cost dashcam solutions combined with deep learning for efficient and cost-effective runway distress detection and management

Developing a traffic control device maintenance management system interfacing with Gis

Roadway systems contain a wide variety of spatially distributed physical features which require installation, maintenance and replacement. These features include traffic control devices such as signs, signals, pavement markings and streetlights. Several technologies exist that can be utilized by the transportation sector to improve program management of a number of these features. Geographic Information Systems (GIS) technology provides a powerful environment for the capture, storage, retrieval, analysis, and display of spatial (locationally defined) data. A need exists to provide an inventory of the transportation physical plant to interface with a work management system. Information pertaining to the number and condition of such features is required for planning, operating, maintaining, managing and budgeting needs. This thesis summarizes the development of a user-friendly, computerized process to establish a graphical interface between a roadway inventory database and GIS; Evaluation of existing technologies and a survey of current literature will provide a basis for the design of a Traffic Control Device Maintenance Management System. This system will provide a consistent form of technology transfer on a common platform. This system will manage resources by integrating work-orders and the database. The system will utilize GIS technology to integrate a work-order system and a database reporting system for resource management. The work order interface capabilities will include maintenance work-order management, project cost and progress tracking, and program planning and policy analysis; The key is to develop a user-friendly system useful to both the field-level installation crews and planning-level management. A case study in Clark County, Nevada, will be used to evaluate alternative methods of collecting and data on traffic control devices and to illustrate the development of a GIS-based management system. This system is intended to improve the efficiency and effectiveness of operational practices as well as serve as a vital decision support tool for planning and management