Pavement Inspection Research Articles

Utilizing Dual Polarized Array GPR System for Shallow Urban Road Pavement Foundation in Environmental Studies: A Case Study

Maintaining the integrity of urban road pavements is vital for public safety, transportation efficiency, and economic stability. However, aging infrastructure and limited budgets make it challenging to detect subsurface defects that can lead to pavement collapses. Traditional inspection methods are often inadequate for identifying such underground anomalies. Ground Penetrating Radar (GPR), especially dual-polarized array systems, offers a non-destructive, high-resolution solution for subsurface inspection. Despite its potential, effectively detecting and analyzing areas at risk of collapse in urban pavements remains a challenge. This study employed a dual-polarized array GPR system to inspect road pavements in London. The research involved comprehensive field testing, including data acquisition, signal processing, calibration, background noise removal, and 3D migration for enhanced imaging. Additionally, Short-Fourier Transform Spectrum (SFTS) analysis was applied to detect moisture-related anomalies. The results show that dual-polarized GPR systems effectively detect subsurface issues like voids, cracks, and moisture-induced weaknesses. The ability to capture data in multiple polarizations improves resolution and depth, enabling the identification of collapse-prone areas, particularly in regions with moisture infiltration. This study demonstrates the practical value of dual-polarized GPR technology in urban pavement inspection, offering a reliable tool for early detection of subsurface defects and contributing to the longevity and safety of road infrastructure.

Classification of Asphalt Pavement Defects for Sustainable Road Development Using a Novel Hybrid Technology Based on Clustering Deep Features

In the rapid development of urbanization, the sustained and healthy development of transportation infrastructure has become a widely discussed topic. The inspection and maintenance of asphalt pavements not only concern road safety and efficiency but also directly impact the rational allocation of resources and environmental sustainability. To address the challenges of modern transportation infrastructure management, this study innovatively proposes a hybrid learning model that integrates deep convolutional neural networks (DCNNs) and support vector machines (SVMs). Specifically, the model initially employs a ShuffleNet architecture to autonomously extract abstract features from various defect categories. Subsequently, the Maximum Relevance Minimum Redundancy (MRMR) method is utilized to select the top 25% of features with the highest relevance and minimal redundancy. After that, SVMs equipped with diverse kernel functions are deployed to perform training and prediction based on the selected features. The experimental results reveal that the model attains a high classification accuracy of 94.62% on a self-constructed asphalt pavement image dataset. This technology not only significantly improves the accuracy and efficiency of pavement inspection but also effectively reduces traffic congestion and incremental carbon emissions caused by pavement distress, thereby alleviating environmental burdens. It is of great significance for enhancing pavement maintenance efficiency, conserving resource consumption, mitigating environmental pollution, and promoting sustainable socio-economic development.

Pavement crack detection based on depth-supervision FRRN model

Timely detection of early pavement distress is crucial for effective pavement maintenance. A promising approach involves the segmenting the road surface cracks area from its images. However, the previously employed full-resolution residual network (FRRN), when employing a large number of full-resolution residual units (FRRUs), is susceptible to challenges such as exploding and vanishing gradients. Addressing these issues, this paper has introduced a depth supervision mechanism into FRRN, leading to the development of the Depth Supervision FRRN (DSFRRN) model, designed specifically for accurate segmentation of cracks in road images. This model enhances nonlinear expressiveness while retaining essential features, achieving high precision in pavement crack segmentation. Through experimental validation using a dataset comprising 1565 images of damaged pavements, the result demonstrate that DSFRRN outperforms the original FRRN in terms of both F1 score and Jaccard index, accurately delineating cracks. Consequently, the automated pavement damage detection method presented herein facilitates cost-effective and efficient pavement inspection, contributing to the safeguarding of roadway surfaces and transportation infrastructure.

Комплексне оцінювання стану дорожнього покриву

Introduction. This article deals with the issue of reviewing existing methods and approaches to assessing the condition of the road pavement and proposes a methodology for assessing the condition of the road pavement using a complex indicator. This indicator is used to analyze the defects and destruction of the pavement determined by the visual method. Problem. The need to improve the methodology for assessing the condition of the road surface (rigid, non-rigid) by calculating the coefficient by which the designer or balance holder can assess the condition of the existing pavement. The purpose of the article. To inform road industry specialists about new trends and developments in the field of road pavement inspection of roads and airfields.

Implementation of a low-cost comprehensive pavement inspection system

Assessing the condition of roads is crucial to the maintenance and rehabilitation process as a country's progress is closely linked to its transport infrastructure. Therefore, it is essential to have well-maintained roads and to be able to control and monitor them properly. Technological advancements have transformed the way pavement inspections are carried out. This study presents an innovative approach that combines stereo cameras and a GPS module for efficient and accurate data collection. This integration of low-cost technologies provides a detailed three-dimensional view of pavements, complemented by accurate geospatial information. The experimental results showed that the 3D images of pavement damage had a relative volume measurement error of 0.80 %. Unlike traditional systems such as LIDAR and ground-penetrating radar, which involve more expensive technologies, the proposed method offers a cost-effective solution. This methodology not only simplifies the inspection process but also improves the planning and execution of road maintenance and repair activities. Its low cost makes it a viable option for various projects and applications in road infrastructure.

Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

Pavement raveling inspection using a new image texture-based feature set and artificial intelligence

Enhancing pavement distress inspection is crucial within a pavement management system. Recognizing the advantages of automated pavement condition inspection, considerable efforts have been invested in developing more efficient systems for this purpose. Raveling, a common asphalt pavement distress, significantly impacts both passenger comfort and pavement durability. Given its connection to pavement texture and the limited effectiveness of earlier inspection systems, improving automatic raveling inspection has become a challenging endeavor. This study proposes an image-based system for raveling detection, employing a new image processing-based feature set. This approach generates enhanced inputs for artificial intelligence models, thereby improving the efficiency of raveling inspection while optimizing resource allocation. To accomplish this, an image dataset is created by combining newly collected images with an existing collection. Novel combinations of features are then engineered to extract meaningful texture-based image features. Subsequently, three feature selection algorithms are employed to identify the most pivotal features. Machine Learning (ML) models are then trained for effective raveling detection based on the proposed feature set, and their performance is evaluated across various performance indices. The achieved performance metrics, reaching nearly 96 % on the test datasets, underscore the efficacy of the proposed method in automating the inspection of texture-based pavement distresses.

Automated detection of pavement distress based on enhanced YOLOv8 and synthetic data with textured background modeling

Regular inspections of pavement distress are essential for accident prevention, and deep learning based algorithms have been developed to ensure high accuracy of inspections. However, the lack of available data is a critical challenge for existing algorithms. To address this issue, an elaborate image synthesis strategy is proposed. By combining textured background modeling for real pavement and Unreal Engine-based distress block stitching technique, high-resolution virtual images that are indistinguishable from real images are generated, including five types of pavement distresses. In addition, an enhanced YOLOv8 network utilizing synthetic data is designed in this paper. The enhanced YOLOv8 network is embedded with the Squeeze and Excitation attention module, and the Swin Transformer module, which are designed to distinguish different types of pavement distress accurately and suppress the interference of complex backgrounds (noise, shadow, blur). The results show that the performance of the algorithms trained based on appropriately ratios of synthetic data (2:1 ratio of virtual to real) improves by more than 10% compared with no virtual data. The enhanced YOLOv8 network achieves a Mean Average Precision (MAP) of 94.8% for transverse cracks, longitudinal cracks, cross-cracks, alligator cracks, and potholes, which is better than seven existing object detection models. The proposed image synthesis method can contribute to improving the accuracy and reliability of pavement inspection and alleviate the reliance on large number of distress samples collection.

Vision-Based Asphalt Pavement Defect Detection Using Deep CNN with BIM Integration

Abstract: The major objective of the research is to predict the defects on the asphalt pavement which is the main concern for the smooth movement of vehicles by using deep CNN. CNN consists of different operations that is image classification, object detection, and image segmentation. We use detectron2 for labelling the defective part to detect the defect accurately. The data set which consists of images of defects of pavement is collected and used for training the algorithm and some of the collected images are used for the testing. The suggested approach makes use of deep learning to automatically detect and categorize a range of asphalt pavement defects, such as surface distress, potholes, and cracks. The training, validation, and testing of the model are conducted on an extensive dataset of annotated pavement photographs. The trained model shows resilience and the ability to generalize, which qualifies it for practical uses in infrastructure management and pavement upkeep. This study advances automated pavement inspection systems, which provide a practical and economical means of determining which maintenance tasks should be prioritized to maintain safer and smoother roads and this approach predicted the defects with an accuracy of 98%.

Deep Learning-Based Road Pavement Inspection by Integrating Visual Information and IMU

This study proposes a deep learning method for pavement defect detection, focusing on identifying potholes and cracks. A dataset comprising 10,828 images is collected, with 8662 allocated for training, 1083 for validation, and 1083 for testing. Vehicle attitude data are categorized based on three-axis acceleration and attitude change, with 6656 (64%) for training, 1664 (16%) for validation, and 2080 (20%) for testing. The Nvidia Jetson Nano serves as the vehicle-embedded system, transmitting IMU-acquired vehicle data and GoPro-captured images over a 5G network to the server. The server recognizes two damage categories, low-risk and high-risk, storing results in MongoDB. Severe damage triggers immediate alerts to maintenance personnel, while less severe issues are recorded for scheduled maintenance. The method selects YOLOv7 among various object detection models for pavement defect detection, achieving a mAP of 93.3%, a recall rate of 87.8%, a precision of 93.2%, and a processing speed of 30–40 FPS. Bi-LSTM is then chosen for vehicle vibration data processing, yielding 77% mAP, 94.9% recall rate, and 89.8% precision. Integration of the visual and vibration results, along with vehicle speed and travel distance, results in a final recall rate of 90.2% and precision of 83.7% after field testing.

Pavement Inspection in Transport Infrastructures Using Unmanned Aerial Vehicles (UAVs)

The growing demand for the transportation of goods and people has led to an increasing reliance on transportation infrastructure, which, in turn, subjects the pavements to high traffic volumes. In order to maintain adequate service and safety standards for users, it is essential to establish effective maintenance strategies that ensure the preservation of pavement conditions. As a result, emerging innovations in pavement surface inspection methods, surpassing traditional techniques in terms of inspection and data processing speed and accuracy, have garnered significant attention. One such groundbreaking innovation in inspection systems that has been tested and used in recent years to assess infrastructure condition is the use of unmanned aerial vehicles (UAVs). This study aims to present a critical open-access literature review on the use of UAVs in the inspection of transportation infrastructure pavement in order to assess the type of equipment used, the technology involved, applicability conditions, data processing, and future evolution. The analysis of relevant literature suggests that the integration of intelligent technologies substantially enhances the accuracy of data collection and the detection of pavement distress. Furthermore, it is evident that most applications and research efforts are oriented towards exploring image processing techniques for the creation of 3D pavement models and distress detection and classification.

Development of a Relationship between Pavement Condition Index and Riding Quality Index on Rural Roads: A Case Study in China

The current standard for evaluating road conditions worldwide relies primarily on the Pavement Condition Index (PCI) and the International Roughness Index (IRI). The IRI can be further calculated to obtain the Riding Quality Index (RQI). To assess pavement damage, various imaging equipment is commonly utilized, providing consistent results that align with actual road conditions. For roughness detection, the Laser Profilometer offers excellent results but may not be suitable for rural roads with poor conditions due to its high inspection cost and the need for a stable environmental setting. Therefore, there is a pressing need to develop cost-effective, rapid, and accurate roughness inspection methods for these roads, which constitute a significant portion of the road network. This study examined the relationship between PCI and RQI using nonlinear regression on 30,088 valid pavement inspection records from various regions in China (totaling 24,624.222 km). Our objective was to estimate RQI solely from PCI data, capitalizing on its broad coverage and superior accuracy. Additionally, we explored how PCI levels impact RQI decay rates. The models in this study were compared to several models published in previous studies at last. Our findings indicate that the model performs best for low-grade roads with low PCI scores, achieving over 90% accuracy for both cement concrete and asphalt concrete pavements. Furthermore, different levels of pavement damage have distinct effects on RQI decay rates, with the most significant impact observed when the pavement is severely damaged. The models in this study outperformed all the other available models in the literature. Consequently, under limited inspection conditions in rural areas, pavement damage inspection results can effectively predict riding quality or roughness, thereby reducing inspection costs. Overall, this study offers valuable insights but has limitations, including limited global generalizability and the model’s applicability to high-grade roads. Future research is needed to address these issues and enhance practical applications.

LTPLN: Automatic pavement distress detection.

Automatic pavement disease detection aims to address the inefficiency in practical detection. However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. Therefore, this paper proposes a novel approach based on the lightweight Transformer Patch Labeling Network (LTPLN) to enhance the efficiency of automatic pavement disease detection and overcome the limitations of existing methods. Firstly, the input images undergo histogram equalization preprocessing to enhance image quality. Subsequently, the images are evenly partitioned into small patch blocks, serving as inputs to the enhanced Transformer model. This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. Furthermore, a depthwise separable convolution module is introduced into the Transformer architecture to introduce convolutional bias and reduce the model's dependence on large amounts of data. Finally, an iterative training process utilizing the label distillation strategy based on expectation maximization is employed to update the labels of patch blocks and roughly locate the positions of pavement diseases under weak supervision. Experimental results demonstrate that compared to the baseline model, the proposed enhanced model achieves a reduction of 2.5G Flops computational complexity and a 16% speed improvement on a private pavement disease dataset, with only a 1.2 percentage point decrease in AUC accuracy. Moreover, compared to other mainstream image classification models, this model exhibits more balanced performance on a public dataset, with improved accuracy and speed that better align with the practical requirements of pavement inspection. These findings highlight the significant performance advantages of the LTPLN model in automatic pavement disease detection tasks, making it more efficiently applicable in real-world scenarios.

Harnessing Deep Learning Techniques for Enhanced Detection and Classification of Cracks in Pavement Imagery

With the increased demand for infrastructure maintenance and the evolving capabilities of deep learning, there is a pressing need to apply advanced techniques for efficient and accurate detection of structural anomalies. This paper delves into the application of Convolutional Neural Networks (CNNs) and transfer learning to address the problem of pavement crack classification using images. Three distinctive models were explored: a 4-layer CNN, a 2-layer CNN, and a VGG-based transfer learning model. Through comprehensive experimentation, we demonstrate the efficacy of these models in accurately classifying Pavement cracks from a curated dataset. Our findings indicate that while each model possesses its unique strengths, the VGG-based transfer learning model exhibits superior performance in terms of precision and recall. This research not only contributes to the growing body of knowledge in infrastructure maintenance using deep learning but also provides practical insights for professionals aiming to employ automated systems for Pavement inspection.

Anomaly Detection Method With Rebar Response Suppression for Microwave Ground Penetrating Radar Pavement Inspection

Anomaly Detection Method With Rebar Response Suppression for Microwave Ground Penetrating Radar Pavement Inspection

Smart monitoring of road pavement deformations from UAV images by using machine learning

Road pavement deformation monitoring is considered the main task for maintenance purposes, especially potholes and cracks, which are the most common types of road deformation surfaces. In order to make pavement inspections more effective, new types of remote sensing data that do not damage the pavement are being used more and more to find pavement distress. This article presents a proposed approach for extracting surface cracks from unmanned aerial vehicle (UAV) images using machine learning, focusing on the data pre-treatment processes. The objective of this study is to evaluate the effectiveness of decision tree classification (DT) in detecting cracks. The performance of the models is also evaluated. The performance evaluation approach is predicated on two primary criteria: model validation and testing. Also, the extent of the impact of post-classification operations, edge detection technology, and morphological processes on crack identification as well as classification accuracy, the digital orthomosaic was generated by the use of a technique commonly referred to as backward projection. To achieve this, the study uses a fusion of gray-level co-occurrence matrix (GLCM) attribute data and RGB images. Cracks are discovered using a classification tree (CT)-based classification approach with an overall classification rate of 86%. Ultimately, morphological processes using the closed image that was formed had a commendable level of accuracy, with an overall classification rate of 96%. The Canny edge detection algorithm has demonstrated its efficacy as a preferred method for detecting cracks from UAV images, providing invaluable decision support for actual road maintenance.

Semi-supervised semantic segmentation using cross-consistency training for pavement crack detection

Crack detection is crucial to providing information for assessing pavement condition and maintaining the safety of infrastructure. Traditional manual crack detection methods are relatively time-consuming and thus have gradually been replaced by automatic detection in recent years. However, most semantic segmentation algorithms require the manual labelling of a large amount of data, which consumes a great deal of time. A semi-supervised semantic word segmentation method based on cross-consistency training for road crack detection is established in this paper. To take advantage of unlabelled crack samples, this study enforced consistency between the primary and secondary decoder predictions, using different disturbed versions of the encoder output as inputs to improve the encoder representation. When only 60% of the annotated data were used, the applied method achieved a better performance than other mainstream semantic segmentation algorithms. Therefore, the applied method can be used in automated and efficient pavement detection for pavement inspection and maintenance.

Improved YOLOv5-Based Real-Time Road Pavement Damage Detection in Road Infrastructure Management

Deep learning has enabled a straightforward, convenient method of road pavement infrastructure management that facilitates a secure, cost-effective, and efficient transportation network. Manual road pavement inspection is time-consuming and dangerous, making timely road repair difficult. This research showcases You Only Look Once version 5 (YOLOv5), the most commonly employed object detection model trained on the latest benchmark Road Damage Dataset, Road Damage Detection 2022 (RDD 2022). The RDD 2022 dataset includes four common types of road pavement damage, namely vertical cracks, horizontal cracks, alligator cracks, and potholes. This paper presents an improved deep neural network model based on YOLOv5 for real-time road pavement damage detection in photographic representations of outdoor road surfaces, making it an indispensable tool for efficient, real-time, and cost-effective road infrastructure management. The YOLOv5 model has been modified to incorporate several techniques that improve its accuracy and generalization performance. These techniques include the Efficient Channel Attention module (ECA-Net), label smoothing, the K-means++ algorithm, Focal Loss, and an additional prediction layer. In addition, a 1.9% improvement in mean average precision (mAP) and a 1.29% increase in F1-Score were attained by the model in comparison to YOLOv5s, with an increment of 1.1 million parameters. Moreover, a 0.11% improvement in mAP and 0.05% improvement in F1 score was achieved by the proposed model compared to YOLOv8s while having 3 million fewer parameters and 12 gigabytes fewer Giga Floating Point Operation per Second (GFlops).

Composite Dielectric Model for Cement Concrete Considering Water Saturation

Water has a significant impact on the dielectric constant of materials. As a multiphase composite material, the effect of water content must be taken into account when modeling the composite dielectric of cement concrete. Cement concrete traditionally has been thought of as a three-phase mixed material consisting of mortar, aggregate (limestone), and pores (air). Water was used as a constituent material of cement concrete in this paper, and its content was described in terms of water saturation. By abstracting the components of cement concrete into independent medium units and arranging them according to certain rules, the four-phase mixing model of cement concrete can be established. To investigate the effect of water, the dielectric constant of cement concrete was measured using a network analyzer at various water saturation levels. The experimental results show that water significantly can enhance the dielectric constant of cement concrete, and this effect is related to porosity: the higher the water saturation, the faster the dielectric constant increases. According to the established concrete four-phase mixed model, combined with the parallel plate capacitor theory, a composite dielectric model considering water saturation was developed which can improve the prediction accuracy under different water saturation conditions and provide a foundation for the application of ground-penetrating radar (GPR) in the quality inspection of cement concrete pavement.

Current research and progress of machine learning in traffic monitoring systems

In modern traffic environments and road construction, road monitoring is one of the most important safeguards for urban safety. Traditional image processing techniques are not suitable for comprehensive data collection and thus accurate and fast feature extraction and generalization of the massive amount of data generated. Real-time surveillance is both tedious and tiring, but vital to safety. This paper describes the traditional image and video processing techniques that have been used to monitor pavements, as well as todays means of image processing for pavement inspection, and summarize the current development based on existing literature research. The result shows that the application of these methods can lead to a better understanding of complex pavement conditions, improved road planning and design and overall safety, and even derive applications to other aspects of computer vision as well.