Accurate and reliable crack segmentation is a challenge and meaningful task.In this article,aiming at the characteristics of cracks on the concrete images,the intensity frequency information of source images which is ...Accurate and reliable crack segmentation is a challenge and meaningful task.In this article,aiming at the characteristics of cracks on the concrete images,the intensity frequency information of source images which is obtained by Discrete Wavelet Transform(DWT)is fed into deep learning-based networks to enhance the ability of network on crack segmentation.To well integrate frequency information into network an effective and novel DWTA module based on the DWT and scSE attention mechanism is proposed.The semantic information of cracks is enhanced and the irrelevant information is suppressed by DWTA module.And the gap between frequency information and convolution information from network is balanced by DWTA module which can well fuse wavelet information into image segmentation network.The Unet-DWTA is proposed to preserved the information of crack boundary and thin crack in intermediate feature maps by adding DWTA module in the encoderdecoder structures.In decoder,diverse level feature maps are fused to capture the information of crack boundary and the abstract semantic information which is beneficial to crack pixel classification.The proposed method is verified on three classic datasets including CrackDataset,CrackForest,and DeepCrack datasets.Compared with the other crack methods,the proposed Unet-DWTA shows better performance based on the evaluation of the subjective analysis and objective metrics about image semantic segmentation.展开更多
Contemporary demands necessitate the swift and accurate detection of cracks in critical infrastructures,including tunnels and pavements.This study proposed a transfer learning-based encoder-decoder method with visual ...Contemporary demands necessitate the swift and accurate detection of cracks in critical infrastructures,including tunnels and pavements.This study proposed a transfer learning-based encoder-decoder method with visual explanations for infrastructure crack segmentation.Firstly,a vast dataset containing 7089 images was developed,comprising diverse conditions—simple and complex crack patterns as well as clean and rough backgrounds.Secondly,leveraging transfer learning,an encoder-decoder model with visual explanations was formulated,utilizing varied pre-trained convolutional neural network(CNN)as the encoder.Visual explanations were achieved through gradient-weighted class activation mapping(Grad-CAM)to interpret the CNN segmentation model.Thirdly,accuracy,complexity(computation and model),and memory usage assessed CNN feasibility in practical engineering.Model performance was gauged via prediction and visual explanation.The investigation encompassed hyperparameters,data augmentation,deep learning from scratch vs.transfer learning,segmentation model architectures,segmentation model encoders,and encoder pre-training strategies.Results underscored transfer learning’s potency in enhancing CNN accuracy for crack segmentation,surpassing deep learning from scratch.Notably,encoder classification accuracy bore no significant correlation with CNN segmentation accuracy.Among all tested models,UNet-EfficientNet_B7 excelled in crack segmentation,harmonizing accuracy,complexity,memory usage,prediction,and visual explanation.展开更多
Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering structures.Most traditional approaches to crack modeling are faced with issues of high computational cos...Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering structures.Most traditional approaches to crack modeling are faced with issues of high computational costs and excessive computing time.To address this issue,we explore the potential of deep learning(DL)to increase the efficiency of crack detection and forecasting crack growth.However,there is no single algorithm that can fit all data sets well or can apply in all cases since specific tasks vary.In the paper,we present DL models for identifying cracks,especially on concrete surface images,and for predicting crack propagation.Firstly,SegNet and U-Net networks are used to identify concrete cracks.Stochastic gradient descent(SGD)and adaptive moment estimation(Adam)algorithms are applied to minimize loss function during iterations.Secondly,time series algorithms including gated recurrent unit(GRU)and long short-term memory(LSTM)are used to predict crack propagation.The experimental findings indicate that the U-Net is more robust and efficient than the SegNet for identifying crack segmentation and achieves the most outstanding results.For evaluation of crack propagation,GRU and LSTM are used as DL models and results show good agreement with the experimental data.展开更多
This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel an...This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.展开更多
In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures.However,the dim and dusty environment inherent to u...In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures.However,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack segmentation.This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural surfaces.Firstly,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional module.Secondly,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small targets.Next,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak cracks.Finally,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target determination.The FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.展开更多
基金National Natural Science Foundation of China under Grant 61972267National Natural Science Foundation of Hebei Province under Grant F2018210148University Science Research Project of Hebei Province under Grant ZD2021334。
文摘Accurate and reliable crack segmentation is a challenge and meaningful task.In this article,aiming at the characteristics of cracks on the concrete images,the intensity frequency information of source images which is obtained by Discrete Wavelet Transform(DWT)is fed into deep learning-based networks to enhance the ability of network on crack segmentation.To well integrate frequency information into network an effective and novel DWTA module based on the DWT and scSE attention mechanism is proposed.The semantic information of cracks is enhanced and the irrelevant information is suppressed by DWTA module.And the gap between frequency information and convolution information from network is balanced by DWTA module which can well fuse wavelet information into image segmentation network.The Unet-DWTA is proposed to preserved the information of crack boundary and thin crack in intermediate feature maps by adding DWTA module in the encoderdecoder structures.In decoder,diverse level feature maps are fused to capture the information of crack boundary and the abstract semantic information which is beneficial to crack pixel classification.The proposed method is verified on three classic datasets including CrackDataset,CrackForest,and DeepCrack datasets.Compared with the other crack methods,the proposed Unet-DWTA shows better performance based on the evaluation of the subjective analysis and objective metrics about image semantic segmentation.
基金the National Natural Science Foundation of China(Grant Nos.52090083 and 52378405)Key Technology R&D Plan of Yunnan Provincial Department of Science and Technology(Grant No.202303AA080003)for their financial support.
文摘Contemporary demands necessitate the swift and accurate detection of cracks in critical infrastructures,including tunnels and pavements.This study proposed a transfer learning-based encoder-decoder method with visual explanations for infrastructure crack segmentation.Firstly,a vast dataset containing 7089 images was developed,comprising diverse conditions—simple and complex crack patterns as well as clean and rough backgrounds.Secondly,leveraging transfer learning,an encoder-decoder model with visual explanations was formulated,utilizing varied pre-trained convolutional neural network(CNN)as the encoder.Visual explanations were achieved through gradient-weighted class activation mapping(Grad-CAM)to interpret the CNN segmentation model.Thirdly,accuracy,complexity(computation and model),and memory usage assessed CNN feasibility in practical engineering.Model performance was gauged via prediction and visual explanation.The investigation encompassed hyperparameters,data augmentation,deep learning from scratch vs.transfer learning,segmentation model architectures,segmentation model encoders,and encoder pre-training strategies.Results underscored transfer learning’s potency in enhancing CNN accuracy for crack segmentation,surpassing deep learning from scratch.Notably,encoder classification accuracy bore no significant correlation with CNN segmentation accuracy.Among all tested models,UNet-EfficientNet_B7 excelled in crack segmentation,harmonizing accuracy,complexity,memory usage,prediction,and visual explanation.
基金The first author would like to thank European Commission H2020-MSCA-RISE BESTOFRAC project for research funding.
文摘Identifying crack and predicting crack propagation are critical processes for the risk assessment of engineering structures.Most traditional approaches to crack modeling are faced with issues of high computational costs and excessive computing time.To address this issue,we explore the potential of deep learning(DL)to increase the efficiency of crack detection and forecasting crack growth.However,there is no single algorithm that can fit all data sets well or can apply in all cases since specific tasks vary.In the paper,we present DL models for identifying cracks,especially on concrete surface images,and for predicting crack propagation.Firstly,SegNet and U-Net networks are used to identify concrete cracks.Stochastic gradient descent(SGD)and adaptive moment estimation(Adam)algorithms are applied to minimize loss function during iterations.Secondly,time series algorithms including gated recurrent unit(GRU)and long short-term memory(LSTM)are used to predict crack propagation.The experimental findings indicate that the U-Net is more robust and efficient than the SegNet for identifying crack segmentation and achieves the most outstanding results.For evaluation of crack propagation,GRU and LSTM are used as DL models and results show good agreement with the experimental data.
基金support from the Ministry of Science and Tech-nology of the:People's Republic of China(Grant No.2021 YFB2600804)the Open Research Project Programme of the State Key Labor atory of Interet of Things for Smart City(University of Macao)(Grant No.SKL-IoTSC(UM)-2021-2023/ORPF/A19/2022)the General Research Fund(GRF)project(Grant No.15214722)from Research Grants Council(RGC)of Hong Kong Special Administrative Re gion Government of China are gratefully acknowledged.
文摘This research developed a hybrid position-channel network (named PCNet) through incorporating newly designed channel and position attention modules into U-Net to alleviate the crack discontinuity problem in channel and spatial dimensions. In PCNet, the U-Net is used as a baseline to extract informative spatial and channel-wise features from shield tunnel lining crack images. A channel and a position attention module are designed and embedded after each convolution layer of U-Net to model the feature interdependencies in channel and spatial dimensions. These attention modules can make the U-Net adaptively integrate local crack features with their global dependencies. Experiments were conducted utilizing the dataset based on the images from Shanghai metro shield tunnels. The results validate the effectiveness of the designed channel and position attention modules, since they can individually increase balanced accuracy (BA) by 11.25% and 12.95%, intersection over union (IoU) by 10.79% and 11.83%, and F1 score by 9.96% and 10.63%, respectively. In comparison with the state-of-the-art models (i.e. LinkNet, PSPNet, U-Net, PANet, and Mask R–CNN) on the testing dataset, the proposed PCNet outperforms others with an improvement of BA, IoU, and F1 score owing to the implementation of the channel and position attention modules. These evaluation metrics indicate that the proposed PCNet presents refined crack segmentation with improved performance and is a practicable approach to segment shield tunnel lining cracks in field practice.
基金This research was funded by the National Key R&D Program of China(Project:Key Technologies and Equipment for Multi-View Stereoscopic Disaster Detection and Emergency Response to Derived Disasters in Underground Spaces,2022YFC3005600)the National Natural Science Foundation of China(52378402)+2 种基金Shandong Provincial Natural Science Foundation Youth Project(ZR2022QE021 and ZR202211100077)Shandong Province Higher Education Young Innovative Team Project(2022KJ037)State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,Jianghan University(PBSKL2022C03),funding from Shandong Railway Investment Holding Group Co.,Ltd.(“Key Technologies for Rapid and Intelligent Construction of Large Section High-Speed Railway Tunnels in Low Mountain and Hilly Areas”and“Intelligent Construction Trolley Equipment and Key Technologies for the Lining of Ultra-Long Open Tunnel Sections”).
文摘In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures.However,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack segmentation.This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural surfaces.Firstly,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional module.Secondly,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small targets.Next,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak cracks.Finally,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target determination.The FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.
