Pavement Images Research Articles

A deeper generative adversarial network for grooved cement concrete pavement crack detection

Periodic grooved cement concrete pavement crack detection is of great importance for pavement condition monitoring and maintenance. The current state-of-the-art (SOTA) detection solutions highly depend on datasets. However, due to the limited access to crack images, more efficient methods are urgently needed to advance the detection of cracking on grooved cement concrete pavement. This study proposes an improved deeper Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to generate datasets of pavement images with a size of 512 × 512 pixels 2. Poisson bleeding is adopted to create the synthesized grooved cement concrete pavement crack images based on the generated crack images and groove images. The robustness of the proposed improved deeper WGAN-GP model is validated by Faster R-CNN, YOLOv3, and YOLOv4 models trained on original crack images and generated crack images for region-level detection. U-Net and W-segnet are used to achieve pixel-level crack detection to evaluate the effectiveness of proposed model. Results show that the improved deeper WGAN-GP could generate more realistic transverse, longitudinal and oblique crack images. In addition, the Poisson bleeding algorithm contributes to synthesizing grooved cement concrete pavement crack images. Moreover, it is observed that YOLOv3 trained by the augmented dataset could achieve a mean average precision (MAP) of 81.98%, 6% MAP higher than the non-augmented dataset. U-Net and W-segnet benefit from augmented dataset with a better pixel-level segmentation result. Based on the results, it can be concluded that the improved deeper WAGN-GP image generation method can provide a straightforward way to fill the data shortage gap of grooved cement concrete pavement cracks, thus increasing the problem-solving capability of the SOTA crack detection models.

Pavement Crack Monitoring: Literature Review

Crack monitoring of pavements is an ever-evolving technology with new crack identification technologies being introduced frequently. Although older technologies consisted of physical removing the pavement section using coring, however new methods are available that are non-destructive and yield a higher performance than conventional technologies. This paper compiles various crack monitoring technologies such as wireless sensor networks, photo imaging, laser imaging, 3D road surface profile scans, acoustics wave propagation technology, embedded strain sensors and onboard vehicle sensors that majorly use an artificial intelligence algorithm to identify and categorize the cracks. The research also includes the use of convolutional neural network that can be used to analyze pavement images and such neural network can localize and classify the cracks for crack initiation and propagation stage. The research concludes with the favor of using the optical imaging technology called Syncrack which serves better performance in terms of time of prediction by 25% and accuracy by 30% when compared to other sensing technologies.

Multi-scale local feature enhanced transformer network for pavement crack detection

目的 道路裂缝是路面病害的早期征兆。定期监测路面状况、及时准确地发现路面裂缝对于交通养护机构降低成本、保证路面结构的可靠性和耐久性以及提高驾驶安全性、舒适性有重要意义。目前基于卷积神经网络的深度学习模型在长距离依赖建模方面存在不足，模型精度难以满足真实路面环境下的裂缝检测任务。一些模型通过引入空间/通道注意力机制进行长距离依赖建模，但是会导致计算量和计算复杂程度增加，无法实现实时检测。鉴于此，本文提出一种基于Transformer编码—解码结构的深度神经网络道路裂缝检测模型CTNet （crack transformernetwork）。方法 该模型主要由Transformer注意力模块、多尺度局部特征增强模块、上采样模块和跨越连接4部分构成。采用Transformer注意力机制能更有效提取全局和长距离依赖关系，克服传统卷积神经网络表征输入信息的短距离相关缺陷。同时，为适应裂缝尺寸变化多样性，将Transformer与多尺度局部特征增强模块相结合，从而有效整合不同尺度局部信息，克服Transformer局部特征建模不足。结果 通过与DeepCrack模型在不同裂缝检测数据集中的比较表明，本文提出的多尺度局部特征增强Transformer网络能快速、准确地分割路面裂缝，且效率更优。定量研究结果表明，CTNet在更有挑战性的CrackLS315数据集中的精度、召回率和F1值达到91.38%、80.38%和85.53%，明显优于对比方法。在CrackWH100数据集中，精度、召回率和F1值进一步提升，分别达到92.70%、90.52%和91.60%。此外，CTNet的训练速率提升至DeepCrack模型的6.78倍。结论 CTNet可以实现强噪声背景下的道路裂缝检测，检测效果优于目前最优方法，且参数量小，易于训练和部署。;Objective The pavement-relevant inspection is focused on the optimization for pavement cracks early-alarming detection and the preservation of pavement structure. However，conventional image processing-based techniques are laborintensive and time-consuming，such as edge detection，threshold segmentation，template matching，and morphology operations. It is challenged to the geometric and spectral complexities of pavement crack and its contexts（e. g. ，illumination variation，oil or water stains，and shadows caused by trees and vehicles）. The convolution neural network（CNN）based deep learning image processing techniques have been developing intensively. However，the CNN-based methods are less effective in long-range dependency modeling，which may cause insufficient detection results in complicated road surface scenarios. Some works are related to attention mechanisms like spatial or channel attention modules，and self-attention modules. However，these attention mechanism-based operations are still challenged for their sophistication and computational cost. Method To detect pavement cracks efficiently and effectively，we develop a novel Transformer-based encoderdecoder neural network，called CTNet，which consists of Transformer blocks，multi-scale local feature enhanced blocks， upsampling blocks，and skip connections. The CTNet can achieve more long-range dependency and global receptive field in terms of multi-head self-attention-based Transformer mechanism. Although Transformer is featured by high running efficiency and low computational overhead demand，it is infeasible to model local contextual information because Token generation can break the connections of neighboring regions. Thus，to capture more multi-scale local information，we design a multi-scale local feature-enhanced block in terms of a multiple dilation ratios-relevant dilation convolution block. Especially，the designed multi-scale local feature enhancement block is melted into each Transformer block for local information complement. Both of local and global low-level contextual features can be captured for feature enhancement. Afterwards，a novel decoder path is implemented to extract high-level features. The decoder consists of the Transformer blocks similar to the up-sampling blocks and the spatial details can be restored for end-to-end segmentation. Result To demonstrate the efficiency and effectiveness of our proposed CTNet，a series of comparative analyses and ablation studies are carried out on three datasets. First，the CTNet can optimize running efficiency，as well as comparable computation overhead and complexity compared to the current UNet，SegNet，DeepCrack，and SwinUnet. Second，CTNet is 6. 78 times faster than the second-best DeepCrack model in terms of training speed. On CrackLS315 dataset，quantitative analyses are also showed that the optimal CTNet is obtained a precision of 91. 38%，a recall of 80. 38%，and a F1 measure of 85. 53% of each；on CrackWH100 dataset，CTNet can obtain a precision of 92. 70%，a recall of 90. 52%，and a F1 measure 91. 60% of each as well. However，it is still challenged to lack of local information when pure-Transformer-based Swin-UNet performed not well compared to fully convolution networks. Furthermore，the CTNet is insufficient to converge when the local blocksenhanced are removed. In summary，the Transformer-based CTNet is beneficial to multi-scenario pavement cracks in terms of the global receptive field. The CTNet can get pavement crack detection results consistently. Conclusion The proposed CTNet has its potentials to deal with noisy pavement images for pavement crack detection.

Research on Data Enhancement Method for Complex Pavement

In the era of intelligent vehicle development, environmental awareness technology as the basis of vehicle decision-making and route planning, affected by the weather, accurate road image recognition is particularly important. Aiming at this problem, this paper constructs a large scale pavement image dataset and proposes an improved image generation method based on CycleGAN data enhancement. The core innovation of the method is the improvement of the model structure, introduce Wassertein distance, and implement global Lipschitz constraint with spectral constraint. The experimental results demonstrate that the accuracy of the data set is up to 91.85%, which is 6.41% higher than the original CycleGAN, and the convergence speed is faster.

A grid‐based classification and box‐based detection fusion model for asphalt pavement crack

AbstractCrack identification is essential for the preventive maintenance of asphalt pavement. Through periodic inspection, the characteristics of existing and emerging cracks can be obtained, and the pavement condition index can be calculated to assess pavement health. The most common method to estimate the area of cracks in an image is to count the number of grid cells or boxes that cover the cracks in an image. Accurate and efficient crack identification is the premise of crack assessment. However, the current patch‐based classification method is limited by the receptive field and cannot be used to directly classify cracks. Furthermore, the postprocessing algorithm in anchor‐based detection is not explicitly optimized for crack topology. In this paper, a new model, which is the fusion of grid‐based classification and box‐based detection based on You Only Look Once version 5 (YOLO v5) is developed and described for the first time. The accuracy and efficiency of the model are high partly due to the implementation of a shared backbone network, multi‐task learning, and joint training. The non‐maximum suppression (NMS)–area‐reduction suppression (ARS) algorithm is presented to filter redundant, overlapping prediction boxes in the postprocessing stage for the crack topology, and the mapping and matching algorithm is proposed to combine the advantages of both the grid‐based and box‐based models. A double‐labeled dataset containing tens of thousands of asphalt pavement images is assembled, and the proposed method is verified on the test set. The fusion model has superior performance over the individual classification and detection models, and the proposed NMS‐ARS algorithm further improves the detection accuracy. Experimental results demonstrate that the presented method effectively realizes automatic crack identification for asphalt pavement.

ConTrack Distress Dataset: A Continuous Observation for Pavement Deterioration Spatio-Temporal Analysis

Analysis of pavement deterioration is critical for road maintenance. Many section-based pavement performance evaluation methodologies have been investigated to determine the deteriorating tendency from a macro perspective. However, little research shed light on the refined deterioration analysis for single distress, which is valuable for daily and preventive maintenance. This paper proposed a deep-learning-based tracking framework to construct a large-scale continuous observation data set for every distress. A deep learning model is applied to detect six types of distress automatically. Then we adopted the spatial clustering method to match the pavement images in the same scene. Finally, image feature matching and perspective conversion methods are adopted to track the distress in the same scene. Using the data collected from the bus driving recorder, we have realized the daily observation of over 270 kilometers of the urban road network. More than 14,000 pavement distress have been continuously tracked, proving this framework’s effectiveness. In addition, the features of pavement deterioration are further discussed. The results show that heavy rain will significantly accelerate road surface deterioration. Under its influence, an intact pavement may suddenly deteriorate into serious potholes within a day. The established continuous pavement distress tracking dataset is significant for distress-level performance prediction research.

Deep-learning based non-contact method for assessing pavement skid resistance using 3D laser imaging technology

ABSTRACT Traditional contact and water-based testing devices exhibit some limitations in evaluating pavement skid resistance. This study demonstrates the viability of deep learning (DL) based non-contact pavement skid resistance evaluation using 3D laser imaging technology. Pavement 3D images and friction numbers were collected from 28 field sites in 10 states in the U.S. to cover pavement sections with various texture and friction characteristics. A total number of 28,000 pairs of 3D images and friction numbers were prepared as the database to train and validate the friction prediction model, named as FrictionNet-V, using the convolutional neural network. The input of FrictionNet-V is the 3D pavement image from the left wheel path, and the output is the corresponding friction number ranging from 0.2 to 0.9. Based on the experimental results on 5,600 testing images, FrictionNet-V achieved better performance than other DL models in terms of speed and accuracy. The result demonstrates the potential of highway speed non-contact pavement skid resistance evaluation using high resolution 3D texture images and DL methods at the network level in the future.

Using Image Processing for Automatic Detection of Pavement Surface Distress

The layers of asphalt pavement can develop a variety of issues and faults, each of which is caused by one or more factors (such as bad mixture design, improper paving techniques, or environmental factors), with traffic being the primary cause of the majority of them. In this paper, a crack detection technique in the pavement image is proposed to address the issue based on the edge information. To accomplish this yhe image is first pre-processed to improve the linear characteristic of the crack by converting the colorful digital image to gray scale using the gray-scale transformation function and the reconstruction filter. The adaptive thresholding method is also designed to map the crack gradient information while coarsely extracting the crack edge based on the grayscale feature. After the filtered edge points have been gathered in line with the gradient information, the edge is recognized using single-pixel filtering processing, which is enhanced by utilizing the local difference between pixels in the fixed region. The complete crack is acquired by filling the crack edge. The proposed method can precisely identify pavement fractures while still maintaining the edge.

An Efficient Method for Detecting Asphalt Pavement Cracks and Sealed Cracks Based on a Deep Data-Driven Model

Thanks to the development of deep learning, the use of data-driven methods to detect pavement distresses has become an active research field. This research makes four contributions to address the problem of efficiently detecting cracks and sealed cracks in asphalt pavements. First, a dataset of pavement cracks and sealed cracks is created, which consists of 10,400 images obtained by a vehicle equipped with a highway condition monitor, with 202,840 labeled distress instances included in these pavement images. Second, we develop a dense and redundant crack annotation method based on the characteristics of the crack images. Compared with traditional annotation, the method we propose generates more object instances, and the localization is more accurate. Next, to achieve efficient crack detection, a semi-automatic crack annotation method is proposed, which reduces the working time by 80% compared with fully manual annotation. Finally, comparative experiments are conducted on our dataset using 13 currently prevailing object detection algorithms. The results show that dense and redundant annotation is effective; moreover, cracks and sealed cracks can be efficiently and accurately detected using the YOLOv5 series model and YOLOv5s is the most balanced model with an F1-score of 86.79% and an inference time of 14.8ms. The pavement crack and sealed crack dataset created in this study is publicly available.

Automatic detection of deteriorated inverted-T patching using 3D laser imaging system based on a true story indiana

Abstract Deteriorated Inverted-T patching can lead to uneven settlement, dip, or reflective transverse cracking on the asphalt overlay. This paper demonstrates a hybrid method to automatically detect deteriorated Inverted-T patching for an efficient maintenance schedule. First, hundreds of 2D/3D pavement images with deteriorated Inverted-T patching were manually identified and labelled from more than 400 miles of field data in Indiana. All data were collected through a high-speed 3D laser imaging system. Afterward, three deep learning architectures, including the Single Shot Detector network (SSD300), an advanced Region-based Convolutional Neural Network (Mask R-CNN), and a fast and precise convolutional network (U-Net), were applied to develop artificial intelligence models to identify deteriorated Inverted-T patching from 3D images. The results indicate that the Mask R-CNN model can achieve good detection accuracy only on the prepared testing images. Further, a hybrid deep learning model was developed to combine International Roughness Index (IRI) values and the corresponding 3D images to detect deteriorated Inverted-T patching. The hybrid method was promising and significantly improved the efficiency of locating deteriorated Inverted-T patching from network screening.

Intelligent pixel‐level detection of multiple distresses and surface design features on asphalt pavements

AbstractSimultaneous pixel‐level detection of multiple distresses and surface design features on complex asphalt pavements is a critical challenge in intelligent pavement survey. This paper proposes a deep‐learning model named ShuttleNet to provide an efficient solution for this challenge by implementing robust semantic segmentation on asphalt pavements. The proposed ShuttleNet aims at repeating the encoding–decoding round freely or even endlessly such that the contexts at different resolution levels can be learned and integrated many times for enhanced latent representations. Additionally, a new and efficient connection method called memory connection is also proposed in the paper and deployed in the ShuttleNet model to provide shortcut connections between successive encoding–decoding rounds. The proposed memory connection can partially or entirely carry the decoded information at different resolution levels into the next encoding–decoding round. Pairing 3D pavement images with 2D pavement images, the proposed ShuttleNet model is applied to detect multiple distresses and surface design features on asphalt pavements simultaneously, including pavement cracks, potholes, sealed cracks, patches, markings, expansion joints, and the pavement background. Experimental results demonstrate that the mean F‐measure and mean intersection‐over‐union attained by the recommended architectural variation of the proposed ShuttleNet model on 1500 testing image pairs are 92.54% and 0.8657 respectively. According to the performance comparisons using both private and public datasets, the proposed ShuttleNet model can yield a noticeably higher detection accuracy, compared with four state‐of‐the‐art models for semantic segmentation.

Self-reconfigurable robot vision pipeline for safer adaptation to varying pavements width and surface conditions

This work presents the vision pipeline for our in-house developed autonomous reconfigurable pavement sweeping robot named Panthera. As the goal of Panthera is to be an autonomous self-reconfigurable robot, it has to understand the type of pavement it is moving in so that it can adapt smoothly to changing pavement width and perform cleaning operations more efficiently and safely. deep learning (DL) based vision pipeline is proposed for the Panthera robot to recognize pavement features, including pavement type identification, pavement surface condition prediction, and pavement width estimation. The DeepLabv3+ semantic segmentation algorithm was customized to identify the pavement type classification, an eight-layer CNN was proposed for pavement surface condition prediction. Furthermore, pavement width estimation was computed by fusing the segmented pavement region on the depth map. In the end, the fuzzy inference system was implemented by taking input as the pavement width and its conditions detected and output as the safe operational speed. The vision pipeline was trained using the DL provided with the custom pavement images dataset. The performance was evaluated using offline test and real-time field trial images captured through the reconfigurable robot Panthera stereo vision sensor. In the experimental analysis, the DL-based vision pipeline components scored 88.02% and 93.22% accuracy for pavement segmentation and pavement surface condition assessment, respectively, and took approximately 10 ms computation time to process the single image frame from the vision sensor using the onboard computer.

Automatic Inspection and Evaluation System for Pavement Distress

Pavement distress detection is of significance for road maintenance and traffic safety. Manual pavement distress detection suffers from high workloads, inefficiency, low accuracy, and high cost. To replace the manual operations in the pre-filling detection with the aim to improve efficiency and reduce cost, this paper proposes a three-stage automatic inspection and evaluation system for pavement distress based on improved deep convolutional neural networks (CNNs). First, the system integrates multi-level context information from the CNN classification model to construct discriminative super-features to determine whether there is distress in the pavement image and the type of the distress, so as to achieve rapid detection of pavement distress. Then, the pavement images with distress are fed into the CNN segmentation model to highlight the distress region with pixel-wise. In the segmentation model, a novel pyramid feature extraction module and a novel guidance attention mechanism are introduced. Finally, we evaluate the degree of pavement damage according to the segmentation results of the CNN segmentation model. In the experiments, we compare our classification model and segmentation model with other state-of-the-art methods on two pavement distress datasets, and the results demonstrate that the proposed models achieve out-performance on different evaluation metrics.

Evaluating Crack Identification Performance of 3D Pavement Imaging Systems Using Portable High-Resolution 3D Scanning

With the increasing adoption of three-dimensional (3D) pavement imaging systems by highway agencies for automated pavement condition assessment, coupled with the advent of diverse systems from different manufacturers, there is a need for standard procedures for the verification and certification of systems’ performance in regard to distress identification, especially for cracking that is a key contributor for triggering maintenance and rehabilitation activities. Although some procedures were adopted by agencies for a rough verification of cracking identification accuracy using ground reference established subjectively by trained raters, a more rigorous and objective method is needed to match the continuous advancement in the systems’ capabilities and data quality requirements. As portable high-resolution 3D scanning technologies have become commercially available, there is an opportunity to leverage them for establishing a more trustable ground reference for the data quality evaluation. This paper proposes a methodology that uses high-resolution 3D scanners to establish the ground reference for field pavement cracking distress to evaluate the crack identification capability of 3D pavement imaging systems in regard to crack quantity, position, and width. A case study was performed by scanning sample pavement cracking spots using “FARO Arm Quantum S” scanner to collect ground reference images and a 3D pavement imaging system installed on the “Georgia Tech Sensing Van” to collect test images to validate the feasibility of the proposed methodology.

Pavement Surface Defect Detection Using Mask Region-Based Convolutional Neural Networks and Transfer Learning

Pavement defect detection is critical for pavement maintenance and management. Meanwhile, the accurate and timely detection of pavement defects in complex backgrounds is a huge challenge for maintenance work. Therefore, this paper used a mask region-based convolutional neural network (Mask R-CNN) and transfer learning to detect pavement defects in complex backgrounds. Twelve hundred pavement images were collected, and a dataset containing corresponding instance labels of the defects was established. Based on this dataset, the performance of the Mask R-CNN was compared with faster region-based convolutional neural networks (Faster R-CNNs) under the transfer of six well-known backbone networks. The results confirmed that the classification accuracy of the two algorithms (Mask R-CNN and Faster R-CNN) was consistent and reached 100%; however, the average precision (AP) of the Mask R-CNN was higher than that of Faster R-CNNs. Meanwhile, the testing time of the models using a feature pyramid network (FPN) was lower than that of other models, which reached 0.21 s per frame (SPF). On this basis, the segmentation performance of the Mask R-CNN was further analyzed at three learning rates (LRs). The Mask R-CNN performed best with ResNet101 plus FPN as its backbone structure, and its AP reached 92.1%. The error rate of defect quantification was between 4% and 16%. It has an ideal detection effect on multi-object and multi-class defects on pavement surfaces, and the quantitative results of the defects can provide a reference for pavement maintenance personnel.

Efficient pavement distress classification via deep patch soft selective learning and knowledge distillation

Pavement distress classification is a vital step for automatic pavement inspection and maintenance. Recently, patch-based approaches have achieved promising performances and thus extensive attention in this field. However, these methods simply assume that all patches contribute equally to the distress classification, leading to weakly discriminating abilities of models. Moreover, their tedious processes also lead to low efficiency in inference. The authors present a novel patch-based pavement distress classification approach named Deep Patch Soft Selective Learning, which addresses these issues. Similar to other patch-based approaches, Deep Patch Soft Selective Learning partitions the pavement images into patches and aggregates the patch features to accomplish the task. To address the first issue, a succinct Soft Patch Feature Selection Network is introduced to assess the importance of each patch to the distress classification with a score based on its feature. These scores will be considered as patch-wise weights for feature aggregation. In such a manner, the most discriminative patches are selected in a soft way, and thereby benefit the final classification. To address the inference efficiency issue, knowledge distillation is leveraged to transfer the classification knowledge from Deep Patch Soft Selective Learning to the image-based approaches, such as EfficientNet-B3. This distilled model enables incorporating both the advantages of patch-based approaches in classification performance and the advantages of image-based approaches in inference efficiency. Extensive experiments on a large-scale pavement image dataset named CQU-BPDD demonstrate that Deep Patch Soft Selective Learning outperforms the baselines by a large margin of +7.4% in P@R on the detection task and +3.1% in F1 on the recognition task while enjoying 2.1x throughput of baselines.

An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement distress detection. The source codes of IOPLIN are released on <uri>https://github.com/DearCaat/ioplin</uri>, and the CQU-BPDD dataset is able to be accessed on <uri>https://dearcaat.github.io/CQU-BPDD/</uri>.

Quantifying Raveling Using 3D Technology with Loss of Aggregates as a New Performance Indicator

Pavement raveling is one of the predominant distresses in the United States that impacts roadway safety and driver comfort on open-graded friction course (OGFC) pavements. Raveling specific treatments, such as fog seal and micro-milling the OGFC layer, can prolong pavement life and reduce resurfacing costs and environmental impact. However, with the current qualitative condition assessment methods (which rate pavements at Severity Levels 1–3 or as light, moderate, or severe), it is difficult to determine the optimal timing for these raveling treatments to be most effective. Therefore, there is an urgent need to develop a method to quantitatively evaluate the raveling condition. While 3D pavement technology provides opportunities for quantifying pavement raveling conditions using 3D pavement surface data, there are two main challenges for quantifying pavement raveling: (1) estimating a reference surface that represents the pavement without any raveling so that the actual pavement can be compared to the reference surface to quantify the raveling, and (2) obtaining pavement images with quantified raveling conditions (aggregate loss volume) for validation. This paper proposes a method with the loss of aggregate as a new performance indicator to automatically quantify raveling using 3D pavement surface data already collected by transportation agencies for pavement evaluation. The proposed method is validated using pavement images (with known aggregate loss) from simulated pavement mats fabricated in the lab and synthetic pavement images obtained by procedural generation. The proposed method consists of (1) 3D data acquisition; (2) pre-processing with (a) outlier removal and image smoothing, (b) two-sensor image stitching, and (c) range image rectification; (3) raveling detection using (a) region of interest selection, (b) reference surface estimation, (c) potential aggregate loss identification, and (d) noise removal; and (4) aggregate loss quantification. The validation results show a strong correlation (R = 0.99) between the computed aggregate loss and the expected aggregate loss. Better performance was observed with the proposed method than with other methods (such as the watershed method and the model fitting method). The proposed method provides a cost-effective means to quantify the loss of aggregates in support of quantitative raveling condition forecasting by leveraging 3D pavement data already collected by transportation agencies.

Gray-Level Image Transformation of Paved Road Cracks with Metaphorical and Computational Analysis

In today’s technology era, the field of digital image processing is growing in popularity and developing demand. When the input is given to the system, after the processing a variation is performed and the output is taken from the system. This research is about a visualized image of road pavement that was first given as an input to obtain an output using gray-level image enhancing techniques. In this research, the image enhancement is performed through different visualized enhancement images, which is the backbone of this research. Initially, it was cogitating as a piece of cake but once we start implementing the technique then we realize that it was not that easy and it needs a lot of knowledge, and the variation of research to overcome complicated problems correlate with this research. The complicated assignments were a variation of light from day to night, visibility of the cracks, homogeneous background, etc. The most complex issue is the visibility and recognition of the road cracks that are complex to classify using different algorithms in obtained images. For deep research purposes, we must identify and categorize many properties of the input images, such as intensity, color adjustment, and conversion for the sake of better image enhancement. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. This depicts the entire outcome of road pavement images to assess the characteristics and repercussions of alteration. To more precisely check road crack detection, image enhancement techniques such as image negative, logarithmic transformations, gamma corrections, and others are applied. Every single specified technique, such as gamma and constant values, is proposed with a general mathematical implementation. Piecewise linear contrast widening subclasses are also studied to more comprehensively discover and assess road cracks. Every technique mentioned above has specific features and a unique image enhancement. Each method has a different and unique feature, so none in all the ways can be implemented for all kinds of gray-level image enhancement. Because in some cases, it will be necessary to implement gamma corrections for better enhancement, while in other areas, it will be necessary to implement logarithmic transformations for the sake of good results. The logarithmic transformation is suitable for such kind of conversion in which logarithm is taken and the image goes under process, but for the same image, the other enhancement like the negative image does not suit. A comparison will be made at the end of this study for different algorithms. We aim to compare and contrast the benefit of one technique over another, and the researcher will implement that technique for better accuracy and result. The proposed framework for image enhancement to classify the road surface cracks is beneficial in discriminating among different types. It also gives us insight into selecting the appropriate technique for the image enhancement of the road cracks according to their kinds.

A night pavement crack detection method based on image‐to‐image translation

AbstractDeep learning provides an efficient automated method for pavement condition surveys, but the datasets used for this model are usually images taken in good lighting conditions. If images are taken at night, this model cannot work effectively. This paper proposes a method for normalizing pavement images at night, which includes three main steps. First, the image feature point detection and matching method is used to process images taken during the day and night. Then, paired images of pavement during the day and night are obtained. Second, with the help of the image‐to‐image translation model, those paired images are used for training, and the best model for converting night images into day images is selected. Third, a convolutional neural network (CNN) based on VGGNet is constructed, and pavement images taken during the day are used for training. After that, six types of images are used and tested separately, namely, those taken during the day and the night, converted by the proposed method and converted by traditional methods. As evaluated by various evaluation indices and visualization methods, the detection performance of the CNN model can be significantly improved by using the proposed method of converted night‐to‐day images.