检索结果-内蒙古大学图书馆

A New Region-Based Minimal Path Selection Algorithm for crack detection and Ground Truth Labeling Exploiting Gabor Filters

引用

REMOTE SENSING 2023年第11期15卷 2722-2722页

作者： de Leon, Gonzalo Fiorentini, Nicholas Leandri, Pietro Losa, Massimo Univ Pisa Dept Civil & Ind Engn DICI Largo Lucio Lazzarino 1 I-56122 Pisa Italy

cracks are fractures or breaks that occur in materials such as concrete, metals, rocks, and other solids. Various methods are used to detect and monitor cracks;among many of them, image-based methodologies allow fast identification of the distress and easy quantification of the percentage of cracks in the scene. Two main categories can be identified: classical and deep learning approaches. In the last decade, the tendency has moved towards the use of the latter. Even though they have proven their outstanding predicting performance, they suffer some drawbacks: a "black-box" nature leaves the user blind and without the possibility of modifying any parameters, a huge amount of labeled data is generally needed, a process that requires expert judgment is always required, and, finally, they tend to be time-consuming. Accordingly, the present study details the methodology for a new algorithm for crack segmentation based on the theory of minimal path selection combined with a region-based approach obtained through the segmentation of texture features extracted using Gabor filters. A pre-processing step is described, enabling the equalization of brightness and shadows, which results in better detection of local minima. These local minimal are constrained by a minimum distance between adjacent points, enabling a better coverage of the cracks. Afterward, a region-based segmentation technique is introduced to determine two areas that are used to determine threshold values used for rejection. This step is critical to generalize the algorithm to images presenting close-up scenes or wide cracks. Finally, a geometrical thresholding step is presented, allowing the exclusion of rounded areas and small isolated cracks. The results showed a very competitive F1-score (0.839), close to state-of-the-art values achieved with deep learning techniques. The main advantage of this approach is the transparency of the workflow, contrary to what happens with deep learning frameworks. In the propo

关键词： minimal path Gabor filter automatic crack detection ground truth labeling deep learning image processing image segmentation image analysis region-based

来源：评论

学校读者我要写书评

暂无评论

Asymmetric dual-decoder-U-Net for pavement crack semantic segmentation

引用

AUTOMATION IN CONSTRUCTION 2023年 156卷

作者： Al-Huda, Zaid Peng, Bo Algburi, Riyadh Nazar Ali Al-antari, Mugahed A. AL-Jarazi, Rabea Al-maqtari, Omar Zhai, Donghai Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu Sichuan Peoples R China Al Farahidi Univ Coll Tech Engn Aeronaut Tech Engn Baghdad Iraq Sejong Univ Coll Software & Convergence Technol Dept Artificial Intelligence Seoul South Korea Southwest Jiaotong Univ Sch Civil Engn Chengdu Sichuan Peoples R China

Accurate pavement crack segmentation is crucial for civil engineering and infrastructure maintenance. To address the challenge of imbalanced data resulting from the prevalence of non-crack pixels, this research seeks to improve the quality of pavement crack segmentation, particularly for thick and tiny cracks. This paper presents an Asymmetric Dual-Decoder-U-Net (ADDU-Net) model, which involves constructing an asymmetric dual decoder with a dual attention module to better capture the features of both thick and tiny cracks under diverse environmental conditions. Through evaluation with images from four benchmark datasets, the ADDUNet model demonstrates its effectiveness and robustness in accurately segmenting various types of cracks. This segmentation model shows significant potential for improving crack segmentation in industrial applications.

关键词： automatic crack detection Semantic segmentation Dual attention U-Net Residual refinement

来源：评论

学校读者我要写书评

暂无评论

CurSeg: A pavement crack detector based on a deep hierarchical feature learning segmentation framework

引用

IET INTELLIGENT TRANSPORT SYSTEMS 2022年第6期16卷 782-799页

作者： Yuan, Genji Li, Jianbo Meng, Xianglong Li, Yinong Qingdao Univ Coll Comp Sci & Technol Qingdao 266000 Peoples R China Qingdao Univ Inst Ubiquitous Networks & Urban Comp Qingdao Peoples R China

automatic crack detection is challenging due to the poor continuity of cracks, the different widths of cracks, and the low contrast between cracks and the surrounding pavement. In this paper, a deep convolutional neural network called CurSeg is proposed, which achieves pixelwise segmentation of cracks in an end-to-end manner. In this approach, features at different scales are fused together to attain the context information from the cracks. The elaborately designed model can effectively suppress the propagation of noise and further refine the crack features by aggregating multiscale and multilevel features from low-level to high-level. Residual detail attention (RDA) is also introduced to better capture the line structure and the ability to accurately locate the crack position in a complex context to make the network more discriminative and robust. CurSeg is evaluated on four datasets to validate the effectiveness of the approach. The experimental results demonstrate that this method achieves state-of-the-art performance on the four challenging datasets.

关键词： automatic crack detection CurSeg multiscale features image segmentation RDA crack position deep convolutional neural network multilevel features residual detail attention feature extraction roads deep learning (artificial intelligence) convolutional neural nets crack features deep hierarchical feature learning segmentation framework pavement crack detector crack detection

来源：评论

学校读者我要写书评

暂无评论

A new image processing strategy for surface crack identification in building structures under non-uniform illumination

引用

IET IMAGE PROCESSING 2022年第2期16卷 407-415页

作者： Parrany, Ahmad Mahdian Mirzaei, Mohsen Vali E Asr Univ Rafsanjan Dept Mech Engn Imam Khomeini Sq Rafsanjan *** Iran

crack detection in building structures is an important approach to evaluating the safety of these structures. However, in some cases under special circumstances, it is very difficult to identify all features of cracks by visual inspection. To tackle this problem and offer a higher potential for practical implementations, developing an automatic crack detection method seems essential. This paper establishes a novel algorithm for surface crack detection in building walls based on image processing techniques to enhance crack visibility. One of the most prevalent issues in surface crack detection from digital crack images is non-uniform illumination of background which makes narrow and tiny cracks indistinguishable. In this regard, an integrated approach, combining adaptive image threshold using local first-order statistics, contrast-limited adaptive histogram equalization, and noise filtering using non-linear diffusion filtering, is proposed to extract the whole skeleton of the crack from digital crack images with non-uniform illumination. Several realistic crack images, suffering from noise and uneven lighting of background, are processed through the proposed algorithm to demonstrate the robustness and high accuracy of the method.

关键词： nonlinear filters adaptive image threshold digital crack images safety building structures realistic crack images local first-order statistics automatic optical inspection statistical analysis nonlinear diffusion filtering tiny cracks nonuniform illumination visual inspection crack visibility narrow cracks feature extraction image filtering image segmentation image enhancement automatic crack detection buildings (structures) realistic images image processing surface crack detection surface cracks building walls contrast-limited adaptive histogram equalization image denoising crack detection structural engineering computing walls structural safety surface crack identification noise filtering

来源：评论

学校读者我要写书评

暂无评论

crack detection algorithm for concrete structures based on super-resolution reconstruction and segmentation network

引用

AUTOMATION IN CONSTRUCTION 2022年 140卷

作者： Xiang, Chao Wang, Wei Deng, Lu Shi, Peng Kong, Xuan Hunan Univ Coll Civil Engn Changsha Peoples R China Hunan Univ Key Lab Damage Diag Engn Struct Hunan Prov Changsha Peoples R China Hunan ZhongDeng Technol Co Ltd Changsha Peoples R China Hunan Univ Key Lab Damage Diag Engn Struct Hunan Prov Changsha 410082 Hunan Peoples R China

crack images collected from civil infrastructures through unmanned aerial vehicles suffer from motion blur and insufficient resolution, which reduces the accuracy of microcrack detection. Therefore, an automatic microcrack detection method based on super-resolution reconstruction (SRR) and semantic segmentation is proposed. Superresolution (SR) images reconstructed by the proposed deep learning-based SRR model were input into the proposed semantic segmentation network for crack segmentation, and the length and width of cracks were measured through an improved medial axis transform approach. The accuracy of crack segmentation and feature quantification for SR images obtained using the deep learning-based SRR is significantly improved compared with low-resolution fuzzy images. The effects of three parameters on the results were analyzed. Compared with the Bicubic testset, the Intersection-over-Union of the SR testset is improved by 17% when a magnification factor of 4 is adopted. The results show that the proposed method achieves good performance in detecting concrete cracks.

关键词： Deep learning Unmanned aerial vehicles Super-resolution reconstruction Semantic segmentation automatic crack detection Magnification factor

来源：评论

学校读者我要写书评

暂无评论

Binocular video-based 3D reconstruction and length quantification of cracks in concrete structures

引用

AUTOMATION IN CONSTRUCTION 2023年第1期148卷

作者： Deng, Lu Sun, Tao Yang, Liang Cao, Ran Hunan Univ Key Lab Damage Diag Engn Struct Hunan Prov Changsha Peoples R China Hunan Univ Coll Civil Engn Changsha Peoples R China CUNY City Coll New York NY USA Hunan Univ Key Lab Damage Diag Engn Struct Hunan Prov Changsha 410082 Hunan Peoples R China

In the past few years, image-based methods for crack inspection have been developed to reduce the cost of the manual crack inspection. However, current image-based methods usually obtain only localized planar crack detection results, while global location and three-dimensional (3D) geometric information of the crack is desired to evaluate the whole structure. In addition, automatic quantification of the actual sizes of all cracks in the global structure is a challenge to overcome. To address these issues, this paper develops an automated 3D reconstruction and length quantification framework for cracks in concrete structures based on binocular videos. In the proposed framework, a crack semantic 3D reconstruction method, combined with binocular visual simultaneous localization and mapping (VSLAM) and a high-performance segmentation network, is first proposed to achieve accurate crack 3D characterization and global localization. A 3D crack quantification method based on point cloud processing is also developed to accurately identify individual cracks on the global structure and quantify their lengths. Field tests are conducted on a concrete bridge to demonstrate the automated inspection framework, which shows high efficiency and practicability. The accuracy of crack detection and quantification of the proposed method is also validated against the results of manual inspection.

关键词： automatic crack detection Three-dimensional reconstruction Semantic segmentation Point cloud processing crack quantification

来源：评论

学校读者我要写书评

暂无评论

Experimental and numerical study on the fracture characteristics of concrete under uniaxial compression

引用

ENGINEERING FRACTURE MECHANICS 2021年 246卷

作者： Ai, Dihao Qiao, Zhen Wu, Yankun Zhao, Yuechao Li, Chengwu Shenzhen Polytech Sch Construct & Environm Engn Shenzhen 518055 Peoples R China China Univ Min & Technol Sch Emergency Management & Safety Engn Beijing 100083 Peoples R China

Since concrete infrastructure may lose its expected physical and functional properties over time, accurate inspection and assessment of such infrastructure systems is necessary to ensure safety and serviceability and prevent unsafe working conditions and the occurrence of hazards. In this paper, an experimental investigation was conducted on several concrete specimens under uniaxial compression to fully explore the fracture process and mechanical characteristics of concrete materials under compressive loading conditions. More specifically, a high-resolution industrial camera and a real-time geophysical acquisition system were employed to capture the fracture process and the microseismic (MS) and electromagnetic emission (EME) signals simultaneously. Based on digital image processing, a robust crack extraction algorithm was proposed for automatically identifying cracks on a concrete surface at the pixel level, and then two novel crack features were introduced to depict the dynamic crack propagation process quantitatively. Then, the correlation between MS, EME, stress drop and dynamic crack evolution characteristics was analyzed. The results indicate that: (1) the MS and EME signals of the concrete specimens have good consistency in the time domain, especially in the fully fractured stage;(2) the crack area, to a certain extent, can reflect the stress drop of a concrete specimen under the compression fracture process;(3) the crack propagation velocity parallel to the loading axis (along the vertical direction) is faster than that the perpendicular one to the loading axis (along the horizontal direction);and (4) the crack area, stress, MS signal, and EME signal reach their peak values nearly simultaneously. Finally, a novel mesh-free numerical model based on peridynamic (PD) theory was established to simulate the fracture process, and the displacement fields were presented to further reveal the failure mechanism of concrete under compressive loading.

关键词： Concrete Microseismic Electromagnetic emissions automatic crack detection crack propagation Peridynamic

来源：评论

学校读者我要写书评

暂无评论

Innovative concepts for surface grinding and automatic inspection of SBQ products

Innovative concepts for surface grinding and automatic inspe...

引用

AISTech 2016 Iron and Steel Technology Conference

作者： Asamer, Norbert BRAUN Maschinenfabrik GmbH Gmundner Strasse 76 Voecklabruck4840 Austria

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：