Crack Segments Research Articles

Investigation of crack segmentation and fast evaluation of crack propagation, based on deep learning

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.

Cascade operation-enhanced high-resolution representation learning for meticulous segmentation of bridge cracks

High-resolution (HR) crack images have proven valuable for bridge inspection using unmanned aerial vehicles (UAVs), offering fine details crucial for accurate segmentation. Traditional deep learning (DL) struggles with HR images due to downsampling issues and limited computational resources. To address this, we propose Cascade-FcaHRNet, a HR representation learning-based multiscale architecture. It incorporates a frequency-channel attention mechanism to capture tiny crack features, a two-stage cascade operation for global and local refinement, and a region-sensitive loss to avoid ambiguous predictions. Ablation studies confirm the effectiveness of these modifications. Robustness experiments show improvements in performance metrics for crack segmentation. In a field test, the Cascade-FcaHRNet accurately segments bridge cracks wider than 0.5 mm from 4 K resolution images, enhancing safety and efficiency in UAV-based bridge inspection. The approach holds potential for developing scientifically sound maintenance and management strategies.

Automatic crack detection on concrete and asphalt surfaces using semantic segmentation network with hierarchical Transformer

In recent studies, deep learning methodologies have shown significant promise in crack detection. However, their practical implementation faces challenges due to the intricate diversity of structural surfaces and the inherent narrowness of cracks. To mitigate these problems, this paper introduces SegFormer, an efficient semantic segmentation model with hierarchical Transformer, for crack detection on concrete and asphalt surfaces in multiple scenarios. The combination of Cross-Entropy (CE) and Dice loss functions is employed to enhance the detection of fine cracks. Additionally, the paper presents an evaluation framework and discusses metrics for assessing crack segmentation results to provide a more precise and comprehensive analysis of model performance. Experimental results indicate that SegFormer outperforms Convolutional Neural Networks (CNNs) such as FCN, U-Net, and DeepLabV3 utilizing different backbones. Notably, the integration of multiple loss functions contributes to a more stable training process, expedites convergence, and yields enhanced results compared to models utilizing individual loss functions.

An average pooling designed Transformer for robust crack segmentation

Crack detection in civil infrastructures has seen impressive accuracy achieved by Convolutional Neural Networks (CNNs) and Transformers. However, practical deployments demand models that are not only highly accurate and robust but also efficient. This paper presents PoolingCrack, a novel and efficient Transformer-based model that leverages a hierarchical structure to capture local and global information in visual data, enabling accurate recovery of crack maps in various conditions. The encoder incorporates an average pooling design that enhances computational efficiency compared to traditional self-attention modules in Transformers, whereas the decoder deploys feature alignment, which improves the feature fusion accuracy. Asphalt, concrete, and masonry crack segmentation results show that the proposed model can reach 0.4% to 6.8% higher mDS than the representative models despite requiring 36–62% fewer parameters and achieving more robustness and effectiveness, with up to 52% higher mDS against noises and other artifacts.

Semantic segmentation of progressive micro-cracking in polymer composites using Attention U-Net architecture

The present study delivers a methodology for investigating the gradual damage development in a carbon fibre-reinforced cross-ply polymer composite during a sequence of thermo-mechanical loadings with the help of X-ray computed tomography. The procedure allows an in-depth analysis of the occurrence and nature of the multiple cracks that form within layers oriented perpendicular, or transverse, to the loading direction. This is achieved by using Attention U-Net architecture for semantic segmentation of the transverse cracks. The model shows promising results, through an ability to identify all the transverse cracks and reflect the damage progression. The described method provides a robust routine for analysing challenging polymer composite tomographic datasets.

Network for robust and high-accuracy pavement crack segmentation

Timely segmentation and repair of pavement cracks significantly impacts both traffic safety and the service life of the roadway. However, current crack segmentation algorithms are limited in terms of imprecise segmentation and poor robustness. To overcome current limitations, this study proposes a pavement crack segmentation algorithm called MixCrackNet. MixCrackNet leverages deformable convolution, weighted loss functions, an efficient multi-scale attention module, and the Mix Structure to identify pavement cracks. Three datasets were used to train and validate the effectiveness of MixCrackNet. By comparing with classical semantic segmentation networks, the results demonstrate that MixCrackNet outperforms all the other models in crack segmentation. Furthermore, MixCrackNet not only exhibits exceptional performance across all three datasets, but also achieves decent results in untrained dataset. These results indicate that MixCrackNet is not only highly accurate but also robust, thereby promoting the application of semantic crack segmentation technology in pavement condition detection.

A multiscale enhanced pavement crack segmentation network coupling spectral and spatial information of UAV hyperspectral imagery

Road pavement cracks are a critical factor affecting the health conditions of road pavements. Accurate crack detection contributes to providing data support for road maintenance measures. Compared to conventional crack detection algorithms, deep learning based crack segmentation methods have practical significance for road maintenance and traffic safety management due to their high precision and automation. However, existing deep learning methods often suffer from segmentation accuracy loss due to the varying crack sizes and the presence of stains on the pavement with spatial characteristics similar to cracks, leading to the misclassification of cracks. Therefore, this study proposed a multiscale enhanced road pavement crack segmentation network (MS-CrackSeg) by coupling spectral and spatial information to detect pavement cracks from unmanned aerial vehicle (UAV) hyperspectral imagery. MS-CrackSeg can simultaneously learn the spatial and rich spectral features of cracks in the hyperspectral imagery, improving the discrimination of those targets with similar spatial features to cracks compared to previous approaches. Moreover, the Multiscale Self-Attention-like Feature Extraction Module (MSSA) is introduced to extract and fuse multiscale crack features to enhance the crack detection. Experiments on a dataset consisting of 1031 hyperspectral images demonstrated the superior crack segmentation of the proposed method compared to the comparative methods. In particular, the proposed method achieved the highest F1-score of 0.74 and mean Intersection over Union of 0.79, indicating an exceptional performance. The developed approach offers improved data support for road maintenance measures and has validated the advantages of UAV hyperspectral imagery in road crack segmentation. The annotated hyperspectral dataset and the code for MS-CrackSeg network are available at https://github.com/williamchen-x/MS-CrackSeg.

A hierarchical federated learning framework for collaborative quality defect inspection in construction

Recent advancements in robotics and deep learning (DL) have made it possible to implement robots in civil infrastructures' quality defect inspection. Robots can reduce human inspectors' workloads and enhance inspection results' reliability by collecting data and automatically identifying quality defects from the raw data. However, current methods for training DL models rely on centralized strategies that require the aggregation of defect data (e.g., uploading to a cloud server), posing concerns about data privacy and security. Thus, this study proposes a three-fold federated learning (FL) framework for training DL models collaboratively, without the need to share local data among construction robots. The framework is specifically applied to image-based crack segmentation, critical for ensuring infrastructures’ safety and serviceability. A lightweight DL model is developed to enable easy implementation on resource-constrained construction robots and to reduce communication costs during federated training. Experimental results show that the proposed FL method outperforms traditional centralized methods. The critical contribution of this study is the hierarchical FL framework, which enables construction robots to leverage big data in a privacy-preserving manner.

The Crack Diffusion Model: An Innovative Diffusion-Based Method for Pavement Crack Detection

Pavement crack detection is of significant importance in ensuring road safety and smooth traffic flow. However, pavement cracks come in various shapes and forms which exhibit spatial continuity, and algorithms need to adapt to different types of cracks while preserving their continuity. To address these challenges, an innovative crack detection framework, CrackDiff, based on the generative diffusion model, is proposed. It leverages the learning capabilities of the generative diffusion model for the data distribution and latent spatial relationships of cracks across different sample timesteps and generates more accurate and continuous crack segmentation results. CrackDiff uses crack images as guidance for the diffusion model and employs a multi-task UNet architecture to predict mask and noise simultaneously at each sampling step, enhancing the robustness of generations. Compared to other models, CrackDiff generates more accurate and stable results. Through experiments on the Crack500 and DeepCrack pavement datasets, CrackDiff achieves the best performance (F1 = 0.818 and mIoU = 0.841 on Crack500, and F1 = 0.841 and mIoU = 0.862 on DeepCrack).

Hyperparameter Optimization and Importance Ranking in Deep Learning–Based Crack Segmentation

Hyperparameter Optimization and Importance Ranking in Deep Learning–Based Crack Segmentation

Deep learning models for crack segmentation in 3d images of concrete trained on semi-synthetic data

Deep learning models for crack segmentation in 3d images of concrete trained on semi-synthetic data

Crack Segmentation of Underwater Structures of Bridges Based on Hierarchical Feature Residual Neural Network

Crack Segmentation of Underwater Structures of Bridges Based on Hierarchical Feature Residual Neural Network

Pulse-coupled neural network based on an adaptive Gabor filter for pavement crack segmentation

This article proposes a Pulse-Coupled Neural Network based on an adaptive Gabor filter for the segmentation of cracks in the pavement in digital images. By estimating the noise in the image, the parameters of the filter that convolves the neurons of the model are adjusted. As a result iterations were reduced to 2%with ? 90% precision. The algorithm was parallelized on the GPU and the processing time was reduced to n/NM regardless of the M and N dimensions of theimage.

A Novel Hybrid Approach for Concrete Crack Segmentation Based on Deformable Oriented-YOLOv4 and Image Processing Techniques

Regular crack inspection plays a significant role in the maintenance of concrete structures. However, most deep-learning-based methods suffer from the heavy workload of pixel-level labeling and the poor performance of crack segmentation with the presence of background interferences. To address these problems, the Deformable Oriented YOLOv4 (DO-YOLOv4) is first developed for crack detection based on the traditional YOLOv4, in which crack features can be effectively extracted by deformable convolutional layers, and the crack regions can be tightly enclosed by a series of oriented bounding boxes. Then, the proposed DO-YOLOv4 is further utilized in combination with the image processing techniques (IPTs), leading to a novel hybrid approach, termed DO-YOLOv4-IPTs, for crack segmentation. The experimental results show that, owing to the high precision of DO-YOLOv4 for crack detection under background noise, the present hybrid approach DO-YOLOv4-IPTs outperforms the widely used Convolutional Neural Network (CNN)-based crack segmentation methods with less labeling work and superior segmentation accuracy.

PCTC-Net: A Crack Segmentation Network with Parallel Dual Encoder Network Fusing Pre-Conv-Based Transformers and Convolutional Neural Networks.

Cracks are common defects that occur on the surfaces of objects and structures. Crack detection is a critical maintenance task that traditionally requires manual labor. Large-scale manual inspections are expensive. Research has been conducted to replace expensive human labor with cheaper computing resources. Recently, crack segmentation based on convolutional neural networks (CNNs) and transformers has been actively investigated for local and global information. However, the transformer is data-intensive owing to its weak inductive bias. Existing labeled datasets for crack segmentation are relatively small. Additionally, a limited amount of fine-grained crack data is available. To address this data-intensive problem, we propose a parallel dual encoder network fusing Pre-Conv-based Transformers and convolutional neural networks (PCTC-Net). The Pre-Conv module automatically optimizes each color channel with a small spatial kernel before the input of the transformer. The proposed model, PCTC-Net, was tested with the DeepCrack, Crack500, and Crackseg9k datasets. The experimental results showed that our model achieved higher generalization performance, stability, and F1 scores than the SOTA model DTrC-Net.

Toward enhancing concrete crack segmentation accuracy under complex scenarios: a novel modified U-Net network

Toward enhancing concrete crack segmentation accuracy under complex scenarios: a novel modified U-Net network

Weakly supervised crack segmentation using crack attention networks on concrete structures

Crack detection or segmentation on concrete structures is a vital process in structural health monitoring (SHM). Though supervised machine learning techniques have gained tremendous success in this domain, data collection and annotation continue to be challenging. Image data collection is challenging, tedious, and laborious, including accessing representative datasets and manually labeling training data in the SHM domain. According to the literature, there are significant issues with the hand-annotation of image data. To address this gap, this paper proposes a two-stage weakly supervised learning framework utilizing a novel “crack attention network (CrANET)” with attention mechanism to detect and segment cracks on images with no human annotations in pixel-level labels. This framework classifies concrete surface images into crack or no-cracks and then uses gradient class activation mapping visualization to generate crack segmentation. Professionals and domain experts subsequently evaluate these segmentation results via a human expert validation study. As the literature suggests that weakly supervised learning is a limited practice in SHM, this research title will motivate researchers in SHM to research and develop a weakly supervised learning approach processing as state of the art.

Semantic segmentation model for concrete cracks based on parallel Swin-CNNs framework

In recent years, crack detection has been the focus of relevant research since concrete fractures are the most dangerous damage to structures. Computer vision-based approaches are frequently employed for their distinct benefits. However, the crack segmentation model based only on convolutional neural networks (CNNs) is still inadequate in generalization because of its inherent bias produced by its low contextual understanding capacity. This research provides a framework PSC (Parallel Swin-CNNs) that employs a multi-scale feature fusion pyramid decoder to partition concrete cracks semantically using Swin Transformer and CNNs. This research evaluates classic CNN models U-Net, U-Net++, DeepLabV3, PSPNet, Feature Pyramid Network (FPN), and DeepCrack on two datasets. The proposed PSC model achieves the best crack segmentation results, with a maximum improvement of 36.57% in F1-score and 62.38% in Intersection over Union value on both datasets, a reduction in parameters of 2.95%–40.89% except for PSPNet. The proposed PSC model demonstrates versatile applicability across various scenarios, effectively overcoming interferences such as light shadows, oil stains, potholes, and textured surfaces while maintaining high computational efficiency.

Photoelectricity Theory-Based Concrete Crack Image Segmentation and Optimal Exposure Interval Research

To solve the problem of low accuracy in automatic concrete crack image segmentation and the non-standardization of concrete crack image datasets, an exposure-based concrete crack image capture scene characterization method was proposed, and the optimal exposure interval for crack segmentation was presented by multiple scene image capture experiments. First, current public crack datasets were collected and analyzed, and it was shown that improper spatial resolution, mislabeling, overexposure, and defocus are frequent non-standardization problems in crack dataset production. Through the analysis of the photoelectric principle in concrete crack imaging, an equivalent exposure was set as a core indicator for scene characterization. Twenty-one indoor scenes were designed by varying the illumination intensity and exposure time, and the experimental results showed that an equivalent exposure can be a core control index for scene characterization. The grayscale distribution law of concrete crack images was analyzed with four specimens’ images captured indoors in 50 exposure scenes, and the segmentation accuracy of an image from each scene was calculated through comparison with corresponding manually labeled binary files. The experiment’s results revealed that 5~50 lx·s was the optimal equivalent exposure interval for concrete crack image segmentation, in which better segmentation accuracy was achieved with an F1 score of up to 96.3%.

Automatic high-precision crack detection of post-earthquake structure based on self-supervised transfer learning method and SegCrackFormer

Accurate crack detection is essential for structural damage assessment after earthquake disasters. However, due to the gap between the target domain of the detected structure and the source domain, it is challenging to achieve high-precision crack segmentation when performing crack detection based on deep learning (DL) in actual engineering. This article proposes a crack segmentation transfer learning method based on a self-supervised learning mechanism and a high-quality pseudo-label generation method, which can significantly improve the detection accuracy in the target domain without pre-made annotations. Besides, to improve the crack segmentation model’s ability to extract local and global features, this article proposes a SegCrackFormer model, which embeds convolutional layers and multi-head self-attention modules. An experiment of the crack segmentation transfer learning method is performed on two open-source crack datasets, METU and Crack500, and a newly proposed LD dataset. The experimental results show that the crack segmentation transfer learning method proposed in this article can improve the mean intersection over union (mIoU) by 38.41% and 15.66% on the Crack500 and LD datasets, respectively. The proposed SegCrackFormer is evaluated through comparative experiments, which demonstrate its superiority over existing crack segmentation models on the METU dataset. Additionally, the proposed method is shown to require significantly less computational resources than other existing models, which highlights the potential of SegCrackFormer as a powerful and efficient model for crack segmentation in practical applications.