The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope ...The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.展开更多
A multi-scale model is developed to link the continuum damage variable in macroscale to the number density of multiple pits and cracks in microscale for studying the corrosion fatigue of aluminum alloy from multi-scal...A multi-scale model is developed to link the continuum damage variable in macroscale to the number density of multiple pits and cracks in microscale for studying the corrosion fatigue of aluminum alloy from multi-scale viewpoint.The developed model is used to predict the coherent multi-scale corrosion fatigue process of aluminum alloy component in the 3.5 wt% NaC1water solution under constant stress amplitude at a nominal frequency of 5Hz, and the numerical prediction results are compared with the experimental results.It shows that the model is effective and can be used to study the corrosion fatigue mechanisms of alurninum alloy from both macro and microscale viewpoints.展开更多
In the diagnosis of rotor crack based on wavelet analysis, it is a painful task to find out an adaptive mother wavelet as many of them can be chosen and the analytic results of different mother wavelets are yet not th...In the diagnosis of rotor crack based on wavelet analysis, it is a painful task to find out an adaptive mother wavelet as many of them can be chosen and the analytic results of different mother wavelets are yet not the same. For this limitation of wavelet analysis, a novel diagnostic approach of rotor crack based on multi-scale singular-spectrum analysis （MS-SSA） is proposed. Firstly, a Jeffcott model of a cracked rotor is developed and the forth-order Runge-Kutta method is used to solve the motion equations of this rotor to obtain its time response （signals）. Secondly, a comparatively simple approach of MS-SSA is presented and the empirical orthogonal functions of different orders in various scales are regarded as analyzing functions. At last, the signals of the cracked rotor and an uncracked rotor are analyzed using the proposed approach of MS-SSA, and the simulative results are compared. The results show that, the data-adaptive analyzing functions can capture many features of signals and the rotor crack can be identified and diagnosed effectively by comparing the analytic results of signals of the cracked rotor with those of the uncracked rotor using the analyzing functions of different orders.展开更多
In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road ...In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road data.Secondly,several basic meshes in the larger scale road network will be merged into a composite one which is matched with one mesh in the smaller scale road network,to complete the N∶1(N>1)and 1∶1 matching.Thirdly,meshes of the two different scale road data with M∶N(M>1,N>1)matching relationships will be matched.Finally,roads will be classified into two categories under the constraints of meshes:mesh boundary roads and mesh internal roads,and then matchings between the two scales meshes will be carried out within their own categories according to the matching relationships.The results show that roads of different scales will be more precisely matched using the proposed method.展开更多
Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalizat...Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.展开更多
Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Cu...Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.展开更多
The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality perc...The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.展开更多
In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strat...In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.展开更多
The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the su...The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.展开更多
In recent years,China's social economy is in a period of rapid development which has also made great progress in the construction of roads and bridges.Roads and bridges as basic building facilities provides strong...In recent years,China's social economy is in a period of rapid development which has also made great progress in the construction of roads and bridges.Roads and bridges as basic building facilities provides strong support for local transportation and socioeconomic development in China.At this stage,roads and bridges are the basic guarantee for the development of the national economy,so great attention must be paid to the quality of their construction and engineering design.At present,the main factor affecting the quality of road bridges in China is the problem of bridge cracks.There are various reasons causes for cracks they must be controlled by effective measures to better improve the quality of road bridges.This article will discuss crack control in road bridge design and construction.展开更多
Road and bridge engineering is an indispensable part of socialist economic construction in China, whose construction quality significantly affects the infrastructure construction level in the whole society. To meet th...Road and bridge engineering is an indispensable part of socialist economic construction in China, whose construction quality significantly affects the infrastructure construction level in the whole society. To meet the rapid economic development of various regions, construction scale and quantity of road and bridge engineering have been continuously expanded and increased, therefore, higher requirements for construction quality and construction standard are also presented. During the construction of road and bridge engineering, concrete crack is a key problem which affects the construction quality. In this regard, this paper analyzes cause and prevention measures of concrete cracks during the construction of road and bridge engineering, and hopes to provide construction personnel with valuable references.展开更多
In the construction of road and bridge, the crack problem is a common construction quality problem, which has a great relationship with construction materials, foundation treatment and other aspects. In order to effec...In the construction of road and bridge, the crack problem is a common construction quality problem, which has a great relationship with construction materials, foundation treatment and other aspects. In order to effectively control the crack problem and improve the service life and safety of roads and bridges, it is very necessary to explore the construction technology and crack treatment measures of roads and bridges. Ensuring people's safe travel and boosting social and economic development. Next, the article discusses the construction technology of roads and bridges and the treatment measures for cracks.展开更多
This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to pr...This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.展开更多
In road and bridge construction, the use of concrete materials has become an important method. Although the construction and application of this concrete material has strong superiority and use value, the problem is m...In road and bridge construction, the use of concrete materials has become an important method. Although the construction and application of this concrete material has strong superiority and use value, the problem is more prominent, especially the occurrence of cracks will seriously affect the safety of roads and bridges, which must arouse people's attention. In view of the most common cracks in road and bridge construction, the main influencing factors and corresponding preventive measures are briefly analyzed and discussed.展开更多
Efficient road distress detection is crucial for transportation safety.To address the challenge of balancing detection accuracy,efficiency,and multi-scale feature fusion in existing methods,this paper proposes a light...Efficient road distress detection is crucial for transportation safety.To address the challenge of balancing detection accuracy,efficiency,and multi-scale feature fusion in existing methods,this paper proposes a lightweight model named MADF-YOLOv8.The model enhances multi-scale feature extraction capability by introducing the Multi-Scale Ghost Residual Convolution(MSGRConv)and the Multiscale Adaptive Feature Processing Module(MAFP).Furthermore,it constructs a Multi-scale Dynamic sampling Bidirectional Feature Pyramid Network(MD-BiFPN)and incorporates the C2f-Faster module to optimize feature fusion efficiency.Experiments on the RDD2022 dataset demonstrate that the proposed model achieves a mean Average Precision at 0.5 Intersection over Union(mAP@0.5)of 88.6%with only 2.312 million parameters.Its overall performance surpasses various mainstream detectors,achieving an exceptional balance between accuracy and efficiency.展开更多
This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two ke...This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two key modules:Constrained Deformable Convolution(CDC),which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets,and the Wavelet Frequency Enhancement Module(WFEM),which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures.Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance,achieving 82.39%mIoU with only 7.49M parameters and 10.34 GFLOPs,outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead.The model also shows strong cross-dataset generalization,achieving 60.01%mIoU and 66.22%F1 on Asphalt3k without fine-tuning.These results highlight CW-HRNet’s favorable accuracyefficiency trade-off for real-world crack segmentation tasks.展开更多
The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road...The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features.To address these issues,this study collected a large number of original road surface images using road detection vehicles.A large-capacity crack dataset was then constructed,with various shapes of cracks categorized as either cracks or fractures.To improve the training performance of the YOLOv5 algorithm,which showed unsatisfactory results on the original dataset,this study used median filtering to preprocess the crack images.The preprocessed images were combined to form the training set.Moreover,the Coordinate Attention(CA)attention module was integrated to further enhance the model’s training performance.The final detection model achieved a recognition accuracy of 88.9%and a recall rate of 86.1%for detecting cracks.These findings demonstrate that the use of image preprocessing technology and the introduction of the CA attention mechanism can effectively detect early-stage pavement cracks that have low contrast with the background.展开更多
Cracks represent a significant hazard to pavement integrity,making their efficient and automated extraction essential for effective road health monitoring and maintenance.In response to this challenge,we propose a cra...Cracks represent a significant hazard to pavement integrity,making their efficient and automated extraction essential for effective road health monitoring and maintenance.In response to this challenge,we propose a crack automatic extraction network model that integrates multi⁃scale image features,thereby enhancing the model’s capability to capture crack characteristics and adaptation to complex scenarios.This model is based on the ResUNet architecture,makes modification to the convolutional layer of the model,proposes to construct multiple branches utilizing different convolution kernel sizes,and adds a atrous spatial pyramid pooling module within the intermediate layers.In this paper,comparative experiments on the performance of the basic model,ablation experiments,comparative experiments before and after data augmentation,and generalization verification experiments are conducted.Comparative experimental results indicate that the improved model exhibits superior detail processing capability at crack edges.The overall performance of the model,as measured by the F1⁃score,reaches 71.03%,reflecting a 2.1%improvement over the conventional ResUNet.展开更多
基金Funded by the National Natural Science Foundation of China Academy of Engineering Physics and Jointly Setup"NSAF"Joint Fund(No.U1430119)。
文摘The multi-scale modeling combined with the cohesive zone model(CZM)and the molecular dynamics(MD)method were preformed to simulate the crack propagation in NiTi shape memory alloys(SMAs).The metallographic microscope and image processing technology were employed to achieve a quantitative grain size distribution of NiTi alloys so as to provide experimental data for molecular dynamics modeling at the atomic scale.Considering the size effect of molecular dynamics model on material properties,a reasonable modeling size was provided by taking into account three characteristic dimensions from the perspective of macro,meso,and micro scales according to the Buckinghamπtheorem.Then,the corresponding MD simulation on deformation and fracture behavior was investigated to derive a parameterized traction-separation(T-S)law,and then it was embedded into cohesive elements of finite element software.Thus,the crack propagation behavior in NiTi alloys was reproduced by the finite element method(FEM).The experimental results show that the predicted initiation fracture toughness is in good agreement with experimental data.In addition,it is found that the dynamics initiation fracture toughness increases with decreasing grain size and increasing loading velocity.
基金Fundamental Research Funds for the Central Universities,Natural Science Foundation of Jiangsu Province (BK20170655,BK20170677),and the National Program on Key Research Project (2016YFC0701301-02).
文摘A multi-scale model is developed to link the continuum damage variable in macroscale to the number density of multiple pits and cracks in microscale for studying the corrosion fatigue of aluminum alloy from multi-scale viewpoint.The developed model is used to predict the coherent multi-scale corrosion fatigue process of aluminum alloy component in the 3.5 wt% NaC1water solution under constant stress amplitude at a nominal frequency of 5Hz, and the numerical prediction results are compared with the experimental results.It shows that the model is effective and can be used to study the corrosion fatigue mechanisms of alurninum alloy from both macro and microscale viewpoints.
基金This project is supported by National Fundamental Research and Development Project Foundation of China(No.G1998020321).
文摘In the diagnosis of rotor crack based on wavelet analysis, it is a painful task to find out an adaptive mother wavelet as many of them can be chosen and the analytic results of different mother wavelets are yet not the same. For this limitation of wavelet analysis, a novel diagnostic approach of rotor crack based on multi-scale singular-spectrum analysis （MS-SSA） is proposed. Firstly, a Jeffcott model of a cracked rotor is developed and the forth-order Runge-Kutta method is used to solve the motion equations of this rotor to obtain its time response （signals）. Secondly, a comparatively simple approach of MS-SSA is presented and the empirical orthogonal functions of different orders in various scales are regarded as analyzing functions. At last, the signals of the cracked rotor and an uncracked rotor are analyzed using the proposed approach of MS-SSA, and the simulative results are compared. The results show that, the data-adaptive analyzing functions can capture many features of signals and the rotor crack can be identified and diagnosed effectively by comparing the analytic results of signals of the cracked rotor with those of the uncracked rotor using the analyzing functions of different orders.
基金The National Natural Science Foundation of China(Nos.4110136241471386)。
文摘In this paper,we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data.The matching process is:Firstly,meshes are extracted from two different scales road data.Secondly,several basic meshes in the larger scale road network will be merged into a composite one which is matched with one mesh in the smaller scale road network,to complete the N∶1(N>1)and 1∶1 matching.Thirdly,meshes of the two different scale road data with M∶N(M>1,N>1)matching relationships will be matched.Finally,roads will be classified into two categories under the constraints of meshes:mesh boundary roads and mesh internal roads,and then matchings between the two scales meshes will be carried out within their own categories according to the matching relationships.The results show that roads of different scales will be more precisely matched using the proposed method.
基金supported in part by the Jiangsu Province Construction System Science and Technology Project(No.2024ZD056)the Research Development Fund of Xi’an Jiaotong-Liverpool University(No.RDF-24-01-097).
文摘Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.
基金This research was funded by the National Key Research and Development Program of China(No.2017YFC1501204)the National Natural Science Foundation of China(No.51678536)+4 种基金the Guangdong Innovative and Entrepreneurial Research Team Program(2016ZT06N340)the Program for Science and Technology Innovation Talents in Universities of Henan Province(Grant No.19HASTIT043)the Outstanding Young Talent Research Fund of Zhengzhou University(1621323001)the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(18IRTSTHN007)the Research on NonDestructive Testing(NDT)and Rapid Evaluation Technology for Grouting Quality of Prestressed Ducts(Contract No.HG-GCKY-01-002).The authors would like to thank for these financial supports.
文摘Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.
基金Hunan Provincial Science and Technology Innovation Leader Project,Grant/Award Number:2021RC4025National Natural ScienceFoundation of China,Grant/Award Number:51808209Hunan Provincial Innovation Foundation for Postgraduate,Grant/Award Number:QL20210106.
文摘The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.
基金founded by National Key R&D Program of China (No.2021YFB2601200)National Natural Science Foundation of China (No.42171416)Teacher Support Program for Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture (No.JDJQ20200307).
文摘In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.
基金the Changsha Science and Technology Plan 2004081in part by the Science and Technology Program of Hunan Provincial Department of Transportation 202117in part by the Science and Technology Research and Development Program Project of the China Railway Group Limited 2021-Special-08.
文摘The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.
文摘In recent years,China's social economy is in a period of rapid development which has also made great progress in the construction of roads and bridges.Roads and bridges as basic building facilities provides strong support for local transportation and socioeconomic development in China.At this stage,roads and bridges are the basic guarantee for the development of the national economy,so great attention must be paid to the quality of their construction and engineering design.At present,the main factor affecting the quality of road bridges in China is the problem of bridge cracks.There are various reasons causes for cracks they must be controlled by effective measures to better improve the quality of road bridges.This article will discuss crack control in road bridge design and construction.
文摘Road and bridge engineering is an indispensable part of socialist economic construction in China, whose construction quality significantly affects the infrastructure construction level in the whole society. To meet the rapid economic development of various regions, construction scale and quantity of road and bridge engineering have been continuously expanded and increased, therefore, higher requirements for construction quality and construction standard are also presented. During the construction of road and bridge engineering, concrete crack is a key problem which affects the construction quality. In this regard, this paper analyzes cause and prevention measures of concrete cracks during the construction of road and bridge engineering, and hopes to provide construction personnel with valuable references.
文摘In the construction of road and bridge, the crack problem is a common construction quality problem, which has a great relationship with construction materials, foundation treatment and other aspects. In order to effectively control the crack problem and improve the service life and safety of roads and bridges, it is very necessary to explore the construction technology and crack treatment measures of roads and bridges. Ensuring people's safe travel and boosting social and economic development. Next, the article discusses the construction technology of roads and bridges and the treatment measures for cracks.
文摘This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.
文摘In road and bridge construction, the use of concrete materials has become an important method. Although the construction and application of this concrete material has strong superiority and use value, the problem is more prominent, especially the occurrence of cracks will seriously affect the safety of roads and bridges, which must arouse people's attention. In view of the most common cracks in road and bridge construction, the main influencing factors and corresponding preventive measures are briefly analyzed and discussed.
基金supported by the National Key Research and Development Program of China under Grant No.2022YFA1602003,entitled"Intelligent Monitoring of Taishan Neutrino Detector".
文摘Efficient road distress detection is crucial for transportation safety.To address the challenge of balancing detection accuracy,efficiency,and multi-scale feature fusion in existing methods,this paper proposes a lightweight model named MADF-YOLOv8.The model enhances multi-scale feature extraction capability by introducing the Multi-Scale Ghost Residual Convolution(MSGRConv)and the Multiscale Adaptive Feature Processing Module(MAFP).Furthermore,it constructs a Multi-scale Dynamic sampling Bidirectional Feature Pyramid Network(MD-BiFPN)and incorporates the C2f-Faster module to optimize feature fusion efficiency.Experiments on the RDD2022 dataset demonstrate that the proposed model achieves a mean Average Precision at 0.5 Intersection over Union(mAP@0.5)of 88.6%with only 2.312 million parameters.Its overall performance surpasses various mainstream detectors,achieving an exceptional balance between accuracy and efficiency.
文摘This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two key modules:Constrained Deformable Convolution(CDC),which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets,and the Wavelet Frequency Enhancement Module(WFEM),which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures.Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance,achieving 82.39%mIoU with only 7.49M parameters and 10.34 GFLOPs,outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead.The model also shows strong cross-dataset generalization,achieving 60.01%mIoU and 66.22%F1 on Asphalt3k without fine-tuning.These results highlight CW-HRNet’s favorable accuracyefficiency trade-off for real-world crack segmentation tasks.
基金jointly supported by the National Natural Science Foundation of China(No.52308332)the China Postdoctoral Science Foundation(Grant No.2022M712787).
文摘The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features.To address these issues,this study collected a large number of original road surface images using road detection vehicles.A large-capacity crack dataset was then constructed,with various shapes of cracks categorized as either cracks or fractures.To improve the training performance of the YOLOv5 algorithm,which showed unsatisfactory results on the original dataset,this study used median filtering to preprocess the crack images.The preprocessed images were combined to form the training set.Moreover,the Coordinate Attention(CA)attention module was integrated to further enhance the model’s training performance.The final detection model achieved a recognition accuracy of 88.9%and a recall rate of 86.1%for detecting cracks.These findings demonstrate that the use of image preprocessing technology and the introduction of the CA attention mechanism can effectively detect early-stage pavement cracks that have low contrast with the background.
基金supported in part by the National Natural Science Foundation of China(No.42401166)the Open Fund of Key Laboratory of Polar Environment Monitoring and Public Governance,Ministry of Education(No.202405)the Key Research and Development Program of Hebei Province(No.23375405D).
文摘Cracks represent a significant hazard to pavement integrity,making their efficient and automated extraction essential for effective road health monitoring and maintenance.In response to this challenge,we propose a crack automatic extraction network model that integrates multi⁃scale image features,thereby enhancing the model’s capability to capture crack characteristics and adaptation to complex scenarios.This model is based on the ResUNet architecture,makes modification to the convolutional layer of the model,proposes to construct multiple branches utilizing different convolution kernel sizes,and adds a atrous spatial pyramid pooling module within the intermediate layers.In this paper,comparative experiments on the performance of the basic model,ablation experiments,comparative experiments before and after data augmentation,and generalization verification experiments are conducted.Comparative experimental results indicate that the improved model exhibits superior detail processing capability at crack edges.The overall performance of the model,as measured by the F1⁃score,reaches 71.03%,reflecting a 2.1%improvement over the conventional ResUNet.
