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Defect detection is an indispensable part of the industrial intelligence process. The introduction of the DETR model marked the successful application of a transformer for defect detection, achieving true end-to-end detection. However, due to the complexity of defective backgrounds, low resolutions can lead to a lack of image detail control and slow convergence of the DETR model. To address these issues, we proposed a defect detection method based on an improved DETR model, called the GM-DETR. We optimized the DETR model by integrating GAM global attention with CNN feature extraction and matching features. This optimization process reduces the defect information diffusion and enhances the global feature interaction, improving the neural network's performance and ability to recognize target defects in complex backgrounds. Next, to filter out unnecessary model parameters, we proposed a layer pruning strategy to optimize the decoding layer, thereby reducing the model's parameter count. In addition, to address the issue of poor sensitivity of the original loss function to small differences in defect targets, we replaced the L1 loss in the original loss function with MSE loss to accelerate the network's convergence speed and improve the model's recognition accuracy. We conducted experiments on a dataset of road pothole defects to further validate the effectiveness of the GM-DETR model. The results demonstrate that the improved model exhibits better performance, with an increase in average precision of 4.9% (mAP@0.5), while reducing the parameter count by 12.9%. [ABSTRACT FROM AUTHOR]

The long segment scheme allowing contractors to oversee road maintenance still has drawbacks. For example, non-compliance, such as delaying preservation for road issues, persists due to small penalties. This leads service providers to neglect road performance without objection to fines. This research aims to provide sufficient incentives for contractors to comply with the implementation of the late delivery penalty rate of road service levels. This research used an experimental method to test two formulas for the late delivery penalty rate of road service levels on two road sections in Central Java and Special Region of Yogyakarta, Indonesia. During the experiment, the time taken by service providers to fulfill the road service level on the two different road sections was measured and recorded. In the first road section, the previously used formula resulted in a penalty of only $7.39, while the developed formula yielded $122.17. Furthermore, in the second road section, the government formula led to a penalty of $375.89, whereas the developed formula resulted in a fine amount of $1,468.99. The results showed that the penalty value given to contractors for two road segment trials using the research formula was 16 and 4 times higher than the formula used by the Directorate General of Highways. In addition, the developed formula takes into account other road performance indicators such as potholes with a diameter < 10 cm and depth < 4 cm, ponding, and uneven patching. [ABSTRACT FROM AUTHOR]

The Portland cement concrete pavement (PCCP) often suffers from different environmental distresses and vehicle load failure, resulting in slab corner fractures, potholes, and other diseases. Rapid repair has become one of the effective ways to open traffic rapidly. In this study, a novel type of rapid repair material, basalt fiber reinforced polymer modified magnesium phosphate cement (BFPMPC), is used to rapidly repair PCCP. Notably, the mechanical properties and characteristics of the repair interfaces which are named interfacial transition zones (ITZs) formed by BFPMPC and cement concrete are focused on as a decisive factor for the performance of the rapid repair. The changing trend of the elastic moduli was studied by nanoindentation experiments in the ITZs with the deconvolution analysis that the elastic moduli of certain kinds of substances can be determined. The experimental results show that the elastic modulus of ITZ-1 with a width of about 20 mm can be regarded as 0.098 times of the aggregate, and 0.51 times of the ordinary Portland cement (OPC) mortar. The BFPMPC-OPC mortar ITZ has roughly the same mechanical properties as the ITZ between aggregate and BFPMPC. A multi-scale representative two-dimensional model was established by random aggregate and a two-dimensional extended finite element method (XFEM) to study the mechanical properties of the repair interface. The simulation results show that the ITZ formed by the interface of BFPMPC and [ABSTRACT FROM AUTHOR]

The persistence of pothole maintenance represents an enduring challenge. Past studies have largely concentrated on the materials and techniques used for remediation, with a lack of attention given to the pothole interface. This paper employed epoxy asphalt rubber (EAR-10) as the repair material, exploring the impact of coupled temperature-dynamic loading on the mechanical response of the interface. Finite element modelling (FEM), adopting the viscoelastic characteristics of EAR-10, was deployed to investigate the mechanical response of the interface under three temperature service conditions high, medium, and low when a dynamic load traversed the pothole. The stress variations in the interface at various inclinations and thicknesses of the repair blocks were also studied. In addition, the comparative analysis of high-temperature rut resistance for powdered rubber composite-modified asphalt and SBS modified asphalt was conducted via the multiple stress examination in terms of its high-temperature resilience, resistance to moisture-induced damage, and fatigue life by employing the asphalt mixture rutting test, low-temperature bending test on small beams, and the water immersion Marshall stability test, respectively. The repair efficacy of EAR-10 was appraised through post-repair water immersion rutting tests and bending tests on composite structural small beams. The results indicated that incorporating coupled temperature-dynamic loading led to a considerable increase in stress, particularly under low-temperature service conditions. An inclination angle of 30 degrees was found to be optimal for the interface. The research methodology presented here is pertinent to guiding the pothole repair in the steel bridge pavement, ensuring the strength and durability of the interface rivals that of newly constructed layers. [ABSTRACT FROM AUTHOR]

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Self-driving vehicles have emerged as a revolutionary breakthrough in modern transportation, promising unparalleled safety, efficiency, and convenience. However, navigating through unpredictable road conditions, especially in the presence of potholes, remains a significant challenge that poses potential safety risks. This study introduces an innovative cloud-powered next-generation self-driving safety system that harnesses the power of AI, specifically YOLOv8 (You Only Look Once version 8), in conjunction with the wandb (Weights & Biases) deep learning platform. This integration enables pothole detection and advanced navigation, elevating the safety standards of autonomous driving. The selection of YOLOv8, a cutting-edge object detection model, is by its exceptional accuracy and speed. YOLOv8 employs a singular neural network to predict object bounding boxes and class probabilities directly, allowing for rapid and precise object detection. This cloud-based architecture also supports continuous model updates and refinements, ensuring the system's adaptability to evolving road conditions and pothole variations. With potential applications extending beyond potholes, this system paves the way for safer and more reliable autonomous transportation, revolutionizing the landscape of self-driving technology. [ABSTRACT FROM AUTHOR]

Road pavements are subject to various forms of degradation compromising their functionality with negative effects on safety. For assuring the highest quality, all the distresses have to be properly identified and quantified by road administrators. For increasing efficiency and reducing costs and times of surveys, several innovative methods to detect, classify and measure surface distresses were proposed, with variable results. In this context, the authors propose an algorithm for automated pothole detection through the processing of 3D data of pavement surfaces, acquired using an innovative high-performance equipment. The algorithm, derived from computer vision, is able of identifying potholes in road sections, assuring a reliable estimation of shape and severity, in terms not only of area, perimeter, but also depth, with practical benefits. The numerical results show the remarkable performance of the proposed algorithm, even compared to alternative traditional methodologies. In terms of Precision, Recall and F-Score, it assures mean values equal respectively to 89.75%, 92.95% 91.28%. Validation was also performed in terms of area error rate, with an average value of 5.15%, significantly lower than other approaches. Then, the algorithm represents a reliable alternative to traditional approaches and allows road administrators to derive data to optimize maintenance and road functionality. [ABSTRACT FROM AUTHOR]

Current pavement pothole repair technology relies heavily on human experience and manual operation. To automate the repair process of pavement pothole spraying repair, a contour-guided zigzag path planning method based on depth image was proposed in this paper, including pothole slicing and slice repair path planning. In the aspect of pothole slicing, a double flood filling method was proposed to ensure the repair strategy from bottom to up under the priority of region by region, while in the aspect of slice path planning, a contour-guided round-trip repair path planning method was proposed to arrange the repair trajectory homogeneously in the pothole without exceeding its irregular boundary. The path planning results showed that the average spacing error of four kinds of common potholes with different shapes was 7.31%, the average time of the whole path planning process was 1.571 s, the average relative difference of the mean square error was only 1.83 mm, which indicated that the proposed method could obtain high path planning accuracy and fast path planning speed. The results demonstrated that the proposed contour-guided zigzag path planning method based on depth image could be applied to the automatic spraying repair of different potholes with convex or concave contours and achieve good pavement repair flatness. [ABSTRACT FROM AUTHOR]

Road infrastructure is essential to national security and growth. Potholes on the road surface cause accidents and costly automotive damage. Novel technology that detects potholes and alerts drivers in real-time may address this challenge. These approaches can improve road safety and lower vehicle maintenance cost in resource-constrained developing nations. This study reviews deep learning and sensor-based pothole detection approaches. An analysis shows that deep learning computer-vision-based algorithms are the most accurate, but computational and economic constraints limit their use in developing nations like Nigeria. Meanwhile, the sensor-based solutions are cost-effective and can be utilized in developing nations for pothole detection. [ABSTRACT FROM AUTHOR]

Automated detection of pavement distress can prevent deterioration of premature surface disintegration in pavements. Potholes that are a common sight in harsh and cold terrains are a severe threat to road safety and a major contributing factor to pavement distress. To facilitate timely detection and repair of potholes, a computationally light and feasible, intelligent pavement pothole detection system is proposed by developing a novel workflow for image-based detection and severity assessment. A single-stage CNN architecture, RetinaNet is modified and optimised to best detect potholes and used in combination with a novel pothole depth estimation algorithm. A comparative evaluation of the model's performance against the existing state-of-the-art model on the benchmark dataset establishes the proposed model's high performance and applicability in real-time scenarios. The depth estimation algorithm is based on a 3D road surface model generated by employing the photogrammetric process of structure from motion (SfM). The point cloud data obtained thereafter, is used for accurate measurement of pothole depth. The comparison of the derived depth with the onsite depth measurement of the pothole reveals a mean error below 5%. This method leads to a practical and intelligent solution to be implemented as part of a potential pavement health assessment system for future practice. [ABSTRACT FROM AUTHOR]

One of crucial activity related to road monitoring and maintenance is the occurrence of potholes. These potholes are also be major reason of road accidents, damaging of vehicles, discomfort of passenger journey and extensive in terms of time and cost. But, identification of potholes can significantly alleviate the aforementioned issues. Other side, the Internet of Things (IoT) plays a crucial role in different applications, and provides viable and state of art solutions for variety of problems. Hence, the aim of this work is to develop a real time automated pothole detection model to detect the potholes in asphalt roads based on IoT devices. The proposed model comprises of three main components such as collection of pothole data and labeling, image preprocessing and texture feature extraction, and extreme gradient boosting (XGBoost) algorithm. The potholes data on asphalt road is collected by three IoT sensors such as accelerometer, ultrasonic sensor, and GPS and further, the collected data is transmitted on cloud via Wi-Fi module. The texture features are extracted using Gaussian steerable and median filters. The extreme gradient boosting (XGBoost) classifier is adopted for prediction task. The simulation results showed that proposed XGBoost model obtains higher accuracy, recall, precision and F1-score rates as 94.56, 97.41, 96.40, and 96.90 respectively using 10-cross fold validation method. [ABSTRACT FROM AUTHOR]

In the context of road transportation, detecting road surface irregularities, particularly potholes, is of paramount importance due to their implications for driving comfort, transportation costs, and potential accidents. This study presents the development of a system for pothole detection using vibration sensors and the Global Positioning System (GPS) integrated within smartphones, without the need for additional onboard devices in vehicles incurring extra costs. In the realm of vibration-based road anomaly detection, a novel approach employing convolutional neural networks (CNNs) is introduced, breaking new ground in this field. An iOS-based application was designed for the acquisition and transmission of road vibration data using the built-in three-axis accelerometer and gyroscope of smartphones. Analog road data were transformed into pixel-based visuals, and various CNN models with different layer configurations were developed. The CNN models achieved a commendable accuracy rate of 93.24% and a low loss value of 0.2948 during validation, demonstrating their effectiveness in pothole detection. To evaluate the performance further, a two-stage validation process was conducted. In the first stage, the potholes along predefined routes were classified based on the labeled results generated by the CNN model. In the second stage, observations and detections during the field study were used to identify road potholes along the same routes. Supported by the field study results, the proposed method successfully detected road potholes with an accuracy ranging from 80% to 87%, depending on the specific route. [ABSTRACT FROM AUTHOR]

The paper presents the results of laboratory tests and the complete results of the implementation of the Sprint fast-setting concrete technology used during the reinstallation of the concrete pavement of the DK50 road (the Młodzieszyn bypass). The problem of concrete pavement repair is related to economic and social costs and, above all, to long repair time. After an extensive analysis of various technologies, the authors created a dedicated solution, which, based on commonly existing materials, allows for a very quick repair of the existing pavement. The obtained properties of the fast-setting concrete allow the maintenance of rheological parameters for 2 h from its production and to install the mixture of the consistency of class S3/S4, while the obtained compressive strength exceeds 25 MPa within 6 h of installation. The concrete parameters obtained after 24 h show a strength exceeding 40 MPa, and after 28 days—exceeding 60 MPa. The tensile strength at shattering exceeds 5 MPa, while the tensile strength at flexural strength exceeds 7 MPa. In addition, all parameters of the fast-setting concrete meet the highest requirements currently set for newly built concrete pavement loaded by very heavy traffic. The most important parameter is the possibility to shorten the repair time of concrete pavement to one day, significantly reducing the social costs associated with closing a single lane or the entire road. An important element of the presented technology is the possibility of producing the mixture at a stationary concrete batching plant, and in the future—with the use of bags for patching potholes in road pavement. [ABSTRACT FROM AUTHOR]

This paper presents a VIDAR (a Vision-IMU based detection and ranging method)-based approach to road-surface pothole detection. Most potholes on the road surface are caused by the further erosion of cracks in the road surface, and tires, wheels and bearings of vehicles are damaged to some extent as they pass through the potholes. To ensure the safety and stability of vehicle driving, we propose a VIDAR-based pothole-detection method. The method combines vision with IMU to filter, mark and frame potholes on flat pavements using MSER to calculate the width, length and depth of potholes. By comparing it with the classical method and using the confusion matrix to judge the correctness, recall and accuracy of the method proposed in this paper, it is verified that the method proposed in this paper can improve the accuracy of monocular vision in detecting potholes in road surfaces. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of the application of computer vision technology in the automatic detection of road potholes and objective assessment of road conditions. Typically, cameras and various types of depth sensors are used to acquire two-dimensional and three-dimensional road data for road imaging. Pothole detection is carried out based on computer vision technology, with the main detection algorithms including classic two-dimensional image processing, threedimensional point cloud modeling and segmentation, deep learning, and their hybrid methods. The hybrid methods, which exploit the advantages of various algorithms, can greatly improve the accuracy of detection. However, while existing algorithms have achieved good results in pothole detection, they still face many challenges, such as the need to improve the robustness of road geometry reconstruction, high algorithm complexity, and the model's strong dependence on large-scale well-annotated datasets. Therefore, future research should focus more on unsupervised stereo matching algorithms and deep learning algorithms with few samples. [ABSTRACT FROM AUTHOR]

Road surface wear leads to the formation of cracks and holes known as potholes. Potholes disrupt the smooth flow of traffic and can lead to accidents. The Intelligent Driver (ID) model is commonly employed but it assumes uniform traffic behavior for all conditions. This oversimplified approach is unrealistic as it does not consider the impact of real-world factors such as potholes on traffic patterns. This paper proposes a microscopic traffic model to address the impact of these road surface irregularities on traffic. The effect of small, medium, and large conical potholes is investigated using fundamental diagrams for traffic flow and velocity. The results obtained indicate that the proposed model outperforms the ID model as it can more accurately characterize how potholes and driver sensitivity affect vehicle behavior. [ABSTRACT FROM AUTHOR]

Featured Application: This research has a direct application to autonomous cars, as well as Advanced Driver Assistance Systems (ADAS). The condition of the roads where cars circulate is of the utmost importance to ensure that each autonomous or manual car can complete its journey satisfactorily. The existence of potholes, speed bumps, and other irregularities in the pavement can cause car wear and fatal traffic accidents. Therefore, detecting and characterizing these anomalies helps reduce the risk of accidents and damage to the vehicle. However, street images are naturally multivariate, with redundant and substantial information, as well as significantly contaminated measurement noise, making the detection of street anomalies more challenging. In this work, an automatic color image analysis using a deep neural network for the detection of potholes on the road using images taken by a ZED camera is proposed. A lightweight architecture was designed to speed up training and usage. This consists of seven properly connected and synchronized layers. All the pixels of the original image are used without resizing. The classic stride and pooling operations were used to obtain as much information as possible. A database was built using a ZED camera seated on the front of a car. The routes where the photographs were taken are located in the city of Celaya in Guanajuato, Mexico. Seven hundred and fourteen images were manually tagged, several of which contain bumps and potholes. The system was trained with 70% of the database and validated with the remaining 30%. In addition, we propose a database that discriminates between potholes and speed bumps. A precision of 98.13% using 37 convolution filters in a 3 × 3 window was obtained, which improves upon recent state-of-the-art work. [ABSTRACT FROM AUTHOR]

In this study, we propose an algorithm to improve the accuracy of tiny object segmentation for precise pothole detection on asphalt pavements. The approach comprises a three-step process: MOED, VAPOR, and Exception Processing, designed to extract pothole edges, validate the results, and manage detected abnormalities. The proposed algorithm addresses the limitations of previous methods and offers several advantages, including wider coverage. We experimentally evaluated the performance of the proposed algorithm by filming roads in various regions of South Korea using a UAV at high altitudes of 30–70 m. The results show that our algorithm outperforms previous methods in terms of instance segmentation performance for small objects such as potholes. Our study offers a practical and efficient solution for pothole detection and contributes to road safety maintenance and monitoring. [ABSTRACT FROM AUTHOR]

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traffic safety is directly affected by poor road conditions. Automating the detection of road defects allows improvements in the maintenance process. The identification of defects such as cracks and potholes can be done using computer vision techniques and supervised learning. In this paper, we propose the detection of cracks and potholes in images of paved roads using machine learning techniques. The images are subdivided into blocks, where Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor Filter's texture descriptors are used to extract features of the images. For the classification task, the Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP) models are compared. We performed two experiments on a dataset built with images of Brazilian highways. In the first experiment, we obtained a F-measure of 75.16% when classifying blocks of images that have cracks and potholes, and 79.56% when comparing roads with defects and without defects. In the second experiment, a F-measure of 87.06% was obtained for the equivalent task. Thus, it is possible to state that the use of the techniques presented is feasible for locating faults in highways. [ABSTRACT FROM AUTHOR]

Poor condition of roads is a major factor for traffic accidents and damage to vehicles. A significant portion of car accidents is attributed to severe three-dimensional (3D) pavement distresses such as potholes, ruttings, and ravelings. Insufficient road condition assessment is responsible for the poor condition of roads. To inspect the condition of the pavement surfaces more frequently and efficiently, an inexpensive data acquisition system was developed that consists of a consumer-grade RGB-D sensor and an edge computing device that can be mounted on vehicles and collect data while driving vehicles. The RGB-D sensor is used for collecting two-dimensional (2D) color images and corresponding 3D depth data, and the lightweight edge computing device is used to control the RGB-D sensor and store the collected data. An RGB-D pavement surface data set is generated. Furthermore, encoder-decoder deep convolutional neural networks (DCNNs) consisting of one or two encoders, and one decoder trained on heterogeneous RGB-D pavement surface data are used for pothole segmentation. Comprehensive experiments using different depth encoding techniques and data fusion methods including data- and feature-level fusion were performed to investigate the efficacy of defect detection using DCNNs. Experimental results demonstrate that the feature-level RGB-D data fusion based on the surface normal encoding of depth data outperform other approaches in terms of segmentation accuracy, where the mean intersection over union (IoU) over 10-fold cross-validation of 0.82 is achieved that shows a 7.7% improvement compared with a network trained only on RGB data. In addition, this study explores the efficacy of indirectly using depth information for pothole detection when depth data are not available. Additionally, the semantic segmentation results were utilized to quantify the severity level of the potholes assisting in maintenance decision-making. The result from these comprehensive experiments using an RGB-D pavement surface data set gathered through the proposed data acquisition system is a stepping stone for opportunistic data collection and processing through crowdsourcing and Internet of Things in future smart cities for effective road assessment. Finally, suggestions about the improvement of the proposed system are discussed. [ABSTRACT FROM AUTHOR]

This paper delves into image detection based on distributed deep-learning techniques for intelligent traffic systems or self-driving cars. The accuracy and precision of neural networks deployed on edge devices (e.g., CCTV (closed-circuit television) for road surveillance) with small datasets may be compromised, leading to the misjudgment of targets. To address this challenge, TensorFlow and PyTorch were used to initialize various distributed model parallel and data parallel techniques. Despite the success of these techniques, communication constraints were observed along with certain speed issues. As a result, a hybrid pipeline was proposed, combining both dataset and model distribution through an all-reduced algorithm and NVlinks to prevent miscommunication among gradients. The proposed approach was tested on both an edge cluster and Google cluster environment, demonstrating superior performance compared to other test settings, with the quality of the bounding box detection system meeting expectations with increased reliability. Performance metrics, including total training time, images/second, cross-entropy loss, and total loss against the number of the epoch, were evaluated, revealing a robust competition between TensorFlow and PyTorch. The PyTorch environment's hybrid pipeline outperformed other test settings. [ABSTRACT FROM AUTHOR]

Road damage detection is essential to the maintenance and management of roads. The morphological road damage contains a large number of multi-scale features, which means that existing road damage detection algorithms are unable to effectively distinguish and fuse multiple features. In this paper, we propose a dense multiscale feature learning Transformer embedding cross-shaped attention for road damage detection (DMTC) network, which can segment the damage information in road images and improve the effectiveness of road damage detection. Our DMTC makes three contributions. Firstly, we adopt a cross-shaped attention mechanism to expand the perceptual field of feature extraction, and its global attention effectively improves the feature description of the network. Secondly, we use the dense multi-scale feature learning module to integrate local information at different scales, so that we are able to overcome the difficulty of detecting multiscale targets. Finally, we utilize a multi-layer convolutional segmentation head to generalize the previous feature learning and get a final detection result. Experimental results show that our DMTC network could segment pavement pothole patterns more accurately and effectively than other methods, achieving an F1 score of 79.39% as well as an OA score of 99.83% on the cracks-and-potholes-in-road-images-dataset (CPRID). [ABSTRACT FROM AUTHOR]

Potholes on roads have been a primary reason for road disasters and damage to the vehicles. Currently due to heavy rains and poor structure accoutrements roads surface have flaws. Detecting the potholes manually is time consuming and will not be detecting the flaw impeccably. The proposed system aim is to recognize potholes on muddy roads and high way roads[1] pictures in order to avoid disasters and damage to the vehicles. Deep learning algorithms are used to classify image dataset in order to determine whether the roads are plain or have potholes. Images are collected from internet sources,(muddy roads) dataset and another dataset is taken from Kaggle (highway roads) dataset. Pretrained models Resnet50, IncepectionV2 and VGG19 are used for training the model. Web application is implemented for testing the model to identify the roads whether there is a pothole or plain based on the selected models like Resnet50, InseptionResNetV2, and VGG19 models are trained for the system. The model performances are analysed for better accuracy, precision and recall. Compare to Resnet50, IncepetionResNetV2 models. VGG19 model has given the best accuracy with 97 percentage for highway roads and 98 percentage for muddy roads. [ABSTRACT FROM AUTHOR]

The development of road transport in Nigeria dates back to 1910, when the existing bush pathways were converted into motorable highways. When the country witnessed a modernized network of important roads for articulated vehicles and passenger automobiles at the conclusion of the Second World War. Since 1975 and via the third and fourth Development Rolling Plans in Nigeria, the government at all levels has used road construction as a tool of pollution control since it impacts people's socioeconomic lives. As a result, many road construction projects are failing long before the worldwide standard lifespan of 20 to 25 years. This study evaluates the carriageway cambering condition, which is one of the primary fundamental elements influencing highway durability. It determines the carriage water drain and road pounding by vehicular weight transfer along the "A2" trunk route linking Lokoja, Abuja, Kaduna, Zaria, and Kano. According to the study, the gradient of all the portions of the road rehabilitated tends to be zero camber, resulting in the occurrence of visible surface folding, potholes, and pounding ln a defined pattern. It is thus recommended that all major road constructions in Nigeria be subjected to cambering tests prior to project delivery and utilisation in order to reduce the economic waste expended on constant repairs of this and other such roads in a country, which mostly transport its goods and services on roads. [ABSTRACT FROM AUTHOR]

Normally, road damages can be automatically detected using image and video data from ground survey vehicle system combined with the detection algorithms. However, there are limitations of scales and map coordinates when using the image and video data to detect potholes. It has been challenging to detect and determine the sizes and locations of potholes. This research utilized a mobile mapping system, MMS, to collect data of roads and environment and classify potholes, roads and other objects. A convolution neural network (CNN) was used to directly identify 3D point clouds using the XYZ method in comparison with the proposed XYZ-RGB method. The XYZ classification demonstrated an overall accuracy of 96.77%, with the intersection over union (IoU) of potholes, roads, and other objects of 59.50%, 94.22%, and 94.06%, respectively. The proposed XYZ-RGB classification indicated an overall accuracy of 97.50%, with the IoU of potholes, roads, and other objects of 66.66%, 95.43%, and 95.42%, respectively. Both datasets were statistically compared at the 95% confidence level, and the results revealed that both classifications produced significantly different results. [ABSTRACT FROM AUTHOR]

The road is the most commonly used means of transportation and serves as a country's arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance. [ABSTRACT FROM AUTHOR]

Road discrepancies such as potholes and road cracks are often present in our day-to-day commuting and travel. The cost of damage repairs caused by potholes has always been a concern for owners of any type of vehicle. Thus, an early detection processes can contribute to the swift response of road maintenance services and the prevention of pothole related accidents. In this paper, automatic detection of potholes is performed using the computer vision model library, You Look Only Once version 3, also known as Yolo v3. Light and weather during driving naturally affect our ability to observe road damage. Such adverse conditions also negatively influence the performance of visual object detectors. The aim of this work was to examine the effect adverse conditions have on pothole detection. The basic design of this study is therefore composed of two main parts: (1) dataset creation and data processing, and (2) dataset experiments using Yolo v3. Additionally, Sparse R-CNN was incorporated into our experiments. For this purpose, a dataset consisting of subsets of images recorded under different light and weather was developed. To the best of our knowledge, there exists no detailed analysis of pothole detection performance under adverse conditions. Despite the existence of newer libraries, Yolo v3 is still a competitive architecture that provides good results with lower hardware requirements. [ABSTRACT FROM AUTHOR]

The Driver Alerting System relies on automatic identification and recognition of traffic signs and potholes. According to recent research, no single approach addresses both pothole detection and traffic sign recognition on roads. The majority of research on pothole detection and traffic sign recognition is based on deep learning techniques such as Convolutional Neural Network, Long Short Time Memory, and others. Moreover, most of the works concentrate on foreign roads where the road condition will be much more different than the Indian roads. In this work, a unified model for recognizing the traffic signs and potholes on Indian Roads is developed. The optimum features related to road traffic signs are extracted and matched using the Hybrid Features From Accelerated Segment Test and Random Sample Consensus algorithms. Features from accelerated segment test is a corner detection technique with high computational efficiency, and the random sample consensus algorithm is applied to discard the mismatching points. To detect potholes, the improved Canny Edge detector and bio-inspired Contour detection method are used. Finally, the Support Vector Machine classifier is used to classify the potholes and traffic signs. The discovered potholes' sizes are then calculated using the bounding box regression model. According to experimental results, the suggested unified model leave behind existing models in terms of accuracy, sensitivity, specificity, Matthews correlation coefficient, and F 1 -Score values. [ABSTRACT FROM AUTHOR]

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Road potholes can cause serious social issues, such as unexpected damages to vehicles and traffic accidents. For efficient road management, technologies that quickly find potholes are required, and thus researches on such technologies have been conducted actively. The three-dimensional (3D) reconstruction method has relatively high accuracy and can be used in practice but it has limited application owing to its long data processing time and high sensor maintenance cost. The two-dimensional (2D) vision method has the advantage of inexpensive and easy application of sensor. Recently, although the 2D vision method using the convolutional neural network (CNN) has shown improved pothole detection performance and adaptability, large amount of data is required to sufficiently train the CNN. Therefore, we propose a method to improve the learning performance of CNN-based object detection model by artificially generating synthetic data similar to a pothole and enhancing the learning data. Additionally, to make the defective areas appear more contrasting, the transformed disparity map (TDM) was calculated using stereo-vision cameras, and the detection performance of the model was further improved through the late fusion with RGB (Red, Green, Blue) images. Consequently, through the convergence of multimodal You Only Look Once (YOLO) frameworks trained by RGB images and TDMs respectively, the detection performance was enhanced by 10.7% compared with that when using only RGB. Further, the superiority of the proposed method was confirmed by showing that the data processing speed was two times faster than the existing 3D reconstruction method. [ABSTRACT FROM AUTHOR]

Potholes in the road are major causes of car accidents, affecting road conditions, driver safety, and vehicle fuel consumption. The timely and effective detection of potholes is a critical step in developing appropriate strategies for driving maintenance and safety. Detecting potholes on Indian road surfaces under diverse climatic conditions, such as potholes covered by tree shadow and filled with water, remains a challenging task. As a result, this research presents a novel method for detecting potholes on Indian roads. This work is being done in two stages. To begin, the Haar feature cascade classifier ensemble is equipped with a sequence of 9-adaptive boosting (Ada-Boost) layers to locate the potholes. Following that, an 11-layer convolutional neural network (CNN) is designed to predict the potholes. Finally, an instance segmentation algorithm is used to mask and quantify the pothole region. The proposed method is trained and tested on Kaggle, CCSAD, and custom-based pothole datasets. For performance assessment of the proposed method, the evaluation metrics, namely sensitivity, precision, recall, F1 score, and mAP, were adopted. It is found that the proposed model detects potholes with an accuracy of 98.65%. The segmentation of 50 test images showed that the Mean Intersection over Unit (MIoU) pothole rate could be 89.52%. [ABSTRACT FROM AUTHOR]

The cold patch asphalt mixtures (CPAMs) have been manufactured by mixing the cold patch asphalt liquid (CPAL) with type LB-13 mineral mixture. The workability, Marshall stability, Freeze–thaw splitting strength and cohesion of three types of CPAMs were tested in the laboratory. The potholes were repaired using CPAMs on several sites in S101 provincial highway at Feidong. The CPAMs were sampled to measure the bulk density and Marshall stability, and the volume of air voids (VV) of those specimens was calculated. The results indicated that the Brookfeld viscosity of CPAL afected the workability of the CPAMs. The Marshall stability of three types of CPAMs was only 3.07, 3.80 and 4.28 kN, respectively. However, the CPAMs had better water stability and met the requirements of hot mix asphalt (HMA) in low-rain regions referring to JTG F40-2004. The VV decreased and Marshall stability increased with the action of the secondary compaction and the volatilization of additives. Beyond 3 months, the Marshall stability of the compacted mixtures tends to be stable. The Marshall stability of type A and B CPAMs changed from 5 to 6 kN, while that of type C CPAM varied between 4 and 5 kN. The products met the requirements of dense-graded HMA on the low-grade highway. Overall, the prepared CPAMs can be used to repair pavement potholes from the perspective of material properties and molding intensity. [ABSTRACT FROM AUTHOR]

This work addressed the issues of poor bonding and low forming strength of emulsified type cold-mix asphalt (CMA). The aim of this study was to improve the pothole repair effect. Accordingly, waterborne epoxy resin (WER) was adopted as an emulsifier, and samples of WER-emulsified CMA reinforced with polypropylene fiber were prepared in the laboratory. First, the mixing ratio of WER and curing agent was optimized through three indexes and storage stability tests. Second, fluorescence microscopy, dynamic shear rheology (DSR), and bending beam rheometer (BBR) tests were conducted to determine the ratio between WER and curing agent, called WERC, in emulsified asphalt binder. After that, pavement performance and bonding characteristics of asphalt mixes produced by this binder were evaluated to analyze the feasibility of using WER-emulsified type CMA in pothole repair and to determine the optimal WERC content. The results show that the best mixing ratio of E-44 and EH-05 is 1.5:1, and WER-emulsified type CMA mixes have shown improved strength, high-temperature rutting resistance, water stability, low-temperature crack resistance, and interfacial bonding characteristics. With the increase of WERC, the performance influence range gradually slows down. Considering overall performance improvement and cost, best WERC content is finally determined as 6.5%. The newly developed emulsified type CMA can be applied to the rapid repair of pavement potholes. [ABSTRACT FROM AUTHOR]

To obtain the road potholes condition timely and effectively for improving the driving safety of intelligent vehicles, the non-contact pothole detection method and the active obstacle avoidance control algorithm were designed based on road pothole detection. The road pothole features were extracted by multi-sensor fusion technology, and area calculation and contour extraction of road potholes were conducted based on visual images. By single point ranging laser radar, the pothole depth was measured. The vehicle model and the road pothole model were built by Adams. According to the evaluation index of human comfort, the limit speeds for different size potholes were determined. The active obstacle avoidance algorithm of road pothole was designed based on the fuzzy control theory. The Simulink/Carsim co-simulation was carried out to verify the algorithm. The results show that the algorithm can meet the system design requirements for controlling the vehicle to safely and comfortably pass through the road potholes and effectively reduce the accident rate of vehicles under dangerous conditions. [ABSTRACT FROM AUTHOR]

The Transport is among the most significant human activities on the planet. Better transportation allows for more trading and a wider distribution of people. Transportation infrastructure like bridges and roads significantly influences the environment and is the primary energy drainer, making transportation sustainability a key concern. This paper also analyses current bridge scour and road potholes detection equipment and methodologies and their effect on the transport system. In scouring regards, a particular emphasis on those uses the structure’s complex reaction to suggest the presence and severity of the scouring phenomena affecting the structure. A Sensitivity Analysis of a newly introduced monitoring system is also assumed. This report examines the similarities and differentials between the bridges scour detection methods and potholes methods. Our key aim is to minimize human effort in identifying road potholes and bridge scouring by using a quick, easy-to-use, cost-effective process, resulting in fewer injuries and economic savings. On the other hand, many initiatives have been taken to create a technology that can instantly identify and detect potholes, leading to improved survey reliability and pavement quality through prior inspection and prompt intervention. [ABSTRACT FROM AUTHOR]

The repeated failure of repaired interface in steel bridge deck pavement (SBDP) under complex service environment makes SBDP maintenance challenging. The primary objective of this paper was to figure out the impact of seepage on repaired interface failure, addressing the secondary damage on SBDP after distresses maintenance. A uni-body bi-material structure (UBS) specimen was fabricated to simulate the repaired interface. Besides, a simulation experimental device of hydrodynamic scouring action was put forward to evaluate the effect of cycle numbers on failure behavior of repaired interface during an indirect tensile test. In addition, numerical simulation analysis was conducted to determine the tensile stress characteristics of repaired interface under load-seepage coupling effect. Findings revealed that the failure state of repaired interface may change from brittle fracture to large deformation damage under seepage effect. The longer the duration of hydrodynamic scouring action, the more serious damage of the repaired interface, with 540 cycles resulting in an approximately 25 % reduction in interface strength. Furthermore, the stress condition of repaired interface is significantly worse when consider the coupling effect of load and seepage, approximately 40 % greater than that of a single load action. And it is closely related to its relative position with the orthotropic steel bridge deck and the actual stress conditions. The research findings presented in this paper can serve as a theoretical foundation for SBDP maintenance. • The interface failure is affected by the properties of materials on both sides. • The seepage effect results in the failure state from brittle to large deformation. • The seepage effect may lead to a sharp decline in repaired interface strength. • The interface suffers from much severe stress condition under seepage effect. [ABSTRACT FROM AUTHOR]

The identification of pavement defects is crucial and must be done efficiently, accurately, and cost-effectively. A commercial-grade sports camera and mobile vehicle work together to detect pavement potholes in a lightweight manner. A dataset of 6,186 images with a resolution of 1500 by 1500 pixels has been constructed based on vehicle-mounted tilted images. Object detection and image segmentation are performed using a single-model MASK R-CNN, and the principle of binocular stereo vision is employed to reconstruct the three-dimensional (3D) structures of potholes. A novel methodology is introduced to calculate the 3D feature parameters of potholes, enabling the precise determination of the pavement pothole damage ratio. An average accuracy of 98% for detection and 94% for segmentation is achieved with the Mask R-CNN model. The proposed algorithms achieve an average DR calculation accuracy of 82%, facilitating precise identification of surface irregularities. The paper presents an intelligent approach for detecting pavement potholes. • Lightweight detection of road potholes is achieved using commercial motion cameras and mobile vehicles working together. • Automated extraction of pothole depth and surface area parameters based on vehicle-mounted tilted images. • A new algorithm of calculating pothole point cloud parameters is developed. • The calculated results of the new algorithm agree with the manually measured results. [ABSTRACT FROM AUTHOR]

To realize the intelligent and accurate measurement of pavement surface potholes, an improved You Only Look Once version three (YOLOv3) object detection model combining data augmentation and structure optimization is proposed in this study. First, color adjustment was used to enhance the image contrast, and data augmentation was performed through geometric transformation. Pothole categories were subdivided into P1 and P2 on the basis of whether or not there was water. Then, the Residual Network (ResNet101) and complete IoU (CIoU) loss were used to optimize the structure of the YOLOv3 model, and the K-Means++ algorithm was used to cluster and modify the multiscale anchor sizes. Lastly, the robustness of the proposed model was assessed by generating adversarial examples. Experimental results demonstrated that the proposed model was significantly improved compared with the original YOLOv3 model; the detection mean average precision (mAP) was 89.3%, and the F1-score was 86.5%. On the attacked testing dataset, the overall mAP value reached 81.2% (−8.1%), which shows that this proposed model performed well on samples after random occlusion and adding noise interference, proving good robustness. [ABSTRACT FROM AUTHOR]