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Virtual reality visualisation of automatic crack detection for bridge inspection from 3D digital twin generated by UAV photogrammetry

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MEASUREMENT 2025年 242卷

作者： Yigit, Abdurahman Yasin Uysal, Murat Mersin Univ Engn Fac Geomat Engn Dept TR-33110 Mersin Turkiye Afyon Kocatepe Univ Engn Fac Geomat Engn Dept TR-03204 Afyonkarahisar Turkiye Afyon Kocatepe Univ Remote Sensing & GIS Applicat & Res Ctr TR-03204 Afyonkarahisar Turkiye

This research develops a UAV photogrammetry methodology to generate 3D digital twins (DT) of the Elvanl & imath;Bridge in Mersin, Turkey. These DTs are then analyzed using advanced image processing algorithms for automatic crack detection. The identified cracks were incorporated into the 3D model, creating a DT to damageaugmented digital twins (DADT). Subsequently, the DADT was rendered in virtual reality, offering an interactive alternative solution with remote access to on-site structural visual inspections in a digital environment. This approach has increased the objectivity and efficiency of inspections in the digital environment. The results demonstrate the potential of this integrated approach to enhance the accuracy and reliability of bridge inspections, reduce operational costs, and provide a comprehensive platform for structural health monitoring through non-invasive methods. The study transformative impact of combining emerging technologies such as UAV photogrammetry and VR in civil engineering, paving the way for more systematic and precise infrastructure assessments.

关键词： UAV photogrammetry Virtual Reality Digital Twins automatic crack detection Bridge Inspection

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automatic crack detection on Road Pavements Using Encoder-Decoder Architecture

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MATERIALS 2020年第13期13卷 2960页

作者： Fan, Zhun Li, Chong Chen, Ying Wei, Jiahong Loprencipe, Giuseppe Chen, Xiaopeng Di Mascio, Paola Shantou Univ Coll Engn Dept Elect & Informat Engn Key Lab Digital Signal & Image Proc Guangdong Pro Shantou 515063 Peoples R China Sapienza Univ Rome Dept Civil Construct & Environm Engn I-00184 Rome Italy Pusan Natl Univ Dept Ind Engn Busan 609735 South Korea

automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can

关键词： pavement cracking automatic crack detection encoder-decoder deep learning U-net hierarchical feature dilated Convolution

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An automatic Pavement crack detection System with Focuscrack Dataset 96

An Automatic Pavement Crack Detection System with FocusCrack...

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IEEE 96th Vehicular Technology Conference (VTC-Fall)

作者： Yan, Xinyun Shi, Shang Xu, Xiaohu He, Zhengran Zhou, Xiaofeng Wang, Chishe Lu, Zhiyi Hohai Univ Coll Comp & Informat Nanjing Peoples R China Jinling Inst Technol Engn Sch Networks & Telecommun Nanjing Peoples R China NJUPT Coll Telecommun & Informat Engn Nanjing Peoples R China Nanjing Great Informat Technol Co Ltd Nanjing Peoples R China

ISBN: (纸本)9781665454681

Road safety has always been one of the main concerns. With the development of deep learning, computer vision has begun to be used in road damage detection. It has the advantages of faster detection speed, lower cost, easier deployment, etc., which greatly reduces traffic accidents caused by the road. We design an automatic detection system for road damages and deploy it on NVIDIA Jetson Xavier NX. A dataset named Focuscrack is collected under diversiform roads and various lighting conditions. It contains six types of diseases, a total of 4181 images and 5812 labels. Compared with performance of several mainstream algorithms like Faster R-CNN and Single Shot MultiBox Detector (SSD), the model adopts the You Only Look Once v5s (YOLOv5s) algorithm. After experimental testing, the precision, recall, mAP@0.5, and mAP@0.5:0.95 are 90.1%, 91.3%, 93.8%, and 51.9%. The system has achieved good results in practical application.

关键词： Pavement cracking automatic crack detection deep learning YOLOv5s

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A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2025年第4期32卷 2455-2486页

作者： Ji, Ankang Xue, Xiaolong Zhang, Limao Luo, Xiaowei Man, Qingpeng Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China Guangzhou Univ Sch Management Guangzhou Peoples R China Huazhong Univ Sci & Technol Natl Ctr Technol Innovat Digital Construct Sch Civil & Hydraul Engn Wuhan Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Harbin Inst Technol Dept Construct Management Harbin Peoples R China

Purpose crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement ***/methodology/approach This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient *** In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 x 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher ***/value The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and e

关键词： automatic crack detection crack feature quantification Transformer TransUnet Deep learning

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Lightweight crack automatic detection Algorithm Based on TF-MobileNet

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APPLIED SCIENCES-BASEL 2024年第19期14卷

作者： Yu, Jiantao Qian, Songrong Chen, Cheng Guizhou Univ Sch Mech Engn Guiyang 550025 Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China

With the progress of social life, the aging of building facilities has become an inevitable phenomenon. The efficiency of manual crack detection is limited, so it is necessary to explore intelligent detection technology. This article proposes a novel crack detection method TF-MobileNet. We took into account the effect of lightweight and crack feature extraction, so we developed a novel crack feature extraction backbone network, which combined Transformer and MobileNetV3. Then we improved the feature fusion network by using the multi-headed attention mechanism of the Bottleneck Transformer, which enables the feature fusion effect to be improved. Then, we integrated SENet and SimAM attention mechanisms into the networks used for feature extraction and feature fusion, thereby further improving the crack detection performance. Finally, we deployed our model in edge devices (NVIDIA Jeston Nano). The findings indicate that our proposed model has achieved 90.8% mAP on the dataset and worked well on the edge device side, which meet the requirements of automatic crack detection. Our model enables real-time monitoring of pavement using edge devices. This approach allows for timely maintenance and repair of the pavement. In the future, we can train the model to recognize more pavement distress features, addressing road safety issues effectively.

关键词： TF-MobileNet automatic crack detection lightweighting attention mechanism edge deployment

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Automated Surface crack detection in Historical Constructions with Various Materials Using Deep Learning-Based YOLO Network

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INTERNATIONAL JOURNAL OF ARCHITECTURAL HERITAGE 2025年第5期19卷 581-597页

作者： Karimi, Narges Mishra, Mayank Lourenco, Paulo B. Univ Minho Dept Civil Engn ISISE ARISE P-4800058 Guimaraes Portugal

Cultural heritage (CH) constructions involve the use of diverse masonry materials. Under natural and human influences, masonry materials can undergo various types of damages, with crack damages being most prevalent. Developing a robust model using deep learning (DL) capable of detecting cracks in various CH materials is crucial. In this study, we compared the performance of the DL method You Only Look Once (YOLO) object detection network based on images in different masonry materials (stone, brick, cob, and tile) with that in a modern material (concrete). The dataset used in the study comprised 1213 bricks, 1116 concretes, 955 cobs, 882 stones, and 208 tile images. YOLOv5 architecture, transfer learning, and object detection models were utilized for detecting cracks and compare their performance in different materials. This study represents the first comparison of this kind using an original dataset. The DL model achieved mean average precision values of 94.4%, 93.9%, 92.7%, 87.2%, 83.4%, 81.6%, and 70.3% for concrete;concrete and cob, cob;stone;stone and brick;brick;and tile, respectively. The findings of this study indicate considerable potential for the widespread use of DL techniques in identifying cracks from images across various CH materials and assisting inspection professionals in damage surveys.

关键词： automatic crack detection convolutional neural networks deep learning historic buildings YOLO

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Pavement crack detection based on local enhancement attention mechanism and deep semantic-guided multi-feature fusion

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JOURNAL OF ELECTRONIC IMAGING 2024年第6期33卷

作者： Li, Jie Li, Huanwen Tu, Jingmin Liu, Zhao Yao, Jian Li, Li Hubei Univ Technol Sch Elect & Elect Engn Wuhan Peoples R China Wuhan Univ Sch Remote Sensing Informat Engn Wuhan Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen Peoples R China

cracks, as a typical type of road defect, are important to be detected and repaired in time for transportation maintenance. However, fine-grained crack detection remains a challenge because of the slender shape, backdrop interference, and noise. We propose an automatic fine-grained crack detection algorithm based on a local enhancement self-attention mechanism and a deep semantic-guided multi-scale fusion strategy. The proposed network is built around an encoder-decoder framework. Inspired by the characteristic of global integrity and local continuity, we employ a Local Enhancement Self-Attention Block (LE Self-AB) in the encoder part. It captures the overall trend of cracks from a global perspective while focusing on the local details in the crack regions. The decoder network adopts a deep semantic-guided multi-scale fusion strategy. This strategy enables the model to benefit from both global and local information through cross-scale feature transfer and fusion. To validate the effectiveness and reliability of our approach, training and evaluation are conducted on the crackTree260 dataset. The generalization of the model is tested on the CRKWH100 and crackLS315 datasets. The experimental results demonstrate that the proposed method outperforms other advanced crack detection methods with an Optimal Dataset Scale of 0.919, 0.918, and 0.894 on three datasets, respectively. (c) 2024 SPIE and IS&T

关键词： automatic crack detection semantic information local enhancement multi-scale feature fusion attention mechanism

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Survey and Investigation on a Coenobium Recently Discovered in Maratea (Italy) with an Innovative Device for crack detection 5th

Survey and Investigation on a Coenobium Recently Discovered ...

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5th International Conference on Protection of Historical Constructions-PROHITECH

作者： Giresini, Linda Imperatore, Stefania Monteleone, Cristina Pitti, Fabio Sapienza Univ Rome Via Eudossiana 18 I-00184 Rome Italy Niccolo Cusano Univ Via Don Carlo Gnocchi 3 I-00166 Rome Italy

ISBN: (纸本)9783031873140;9783031873126;9783031873119

In the Christian period, Maratea (Southern Italy) became a center for Italo-Greek monasticism, encouraging Christians to live as hermits in caves or monastic settlements. The fourth Author recently discovered there seven cenobitic complexes and chapels probably dating to the IX-XI century AD. This paper presents preliminary investigations on one of these newly discovered sites with a focus on its architectural and structural features. The study details the spatial layout and materials of the site, alongside an analysis of existing crack patterns. Additionally, it presents the experimental use of a sensor prototype capable of automatically identifying and performing non-contact measurements of crack widths. This device, developed under the scientific support and supervision of Niccolo Cusano University and CNR-ISPC, has been used for the first time out of laboratory to test the device effectiveness in field conditions, identifying any challenges related to its use in an outdoor environment. The initial results indicate a promising application for this tool, used to investigate an area of unique historical value capable of widening the knowledge of the Italo-Greek monasticism developed in Southern Italy.

关键词： coenobium monastery cultural heritage masonry no contact measurements automatic crack detection

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Novel R-C-C fusion machine learning method for automatic damage detection of heritage buildings using 3D point cloud data

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2025年 1-18页

作者： Seo, Hyungjoon Sihag, Paramjeet Fu, Lang Kim, Dongku Seoul Natl Univ Sci & Technol Dept Civil Engn Seoul 01811 South Korea Univ Liverpool Dept Civil & Environm Engn Liverpool England Korea Inst Civil Engn & Bldg Technol Dept Geotech Engn Res Goyang 10223 Gyeonggi Do South Korea

With the advancements in 3D laser scanning technology, a more efficient and accurate approach to crack detection can be achieved. This study presents a novel crack detection called by R-C-C method. The technique utilizes a 3D laser scanner to obtain point cloud data of a structure. The point cloud is then processed to calculate its roughness and establish a minimum roughness level to reduce the cloud's density. The CANUPO classifier is trained using crack and joint samples as training data. The classifier can differentiate between the two classes and further trains to identify cracks in the point cloud. The results are cracks exported as a point cloud, providing a reliable and efficient method for crack detection. The proposed crack detection method is innovative and provides a more efficient and accurate approach than traditional manual inspection methods. About 93.10% of points were reduced through roughness analysis, which can minimize the analysis time, which is the biggest disadvantage of point cloud analysis. The first CANUPO of R-C-C removes points corresponding to most bricks through a machine learning classifier. However, the remaining points through the first CANUPO include points including joints as well as cracks. Based on these results, the second CANUPO conducted, which focused on separating joints and cracks through learning. The second CANUPO was able to locate the exact location of the crack after isolating the joint and crack. The study's results demonstrate the proposed method's feasibility and effectiveness in detecting cracks in masonry structures.

关键词： Laser scanning Point clouds Machine learning automatic crack detection Heritage building R-C-C method

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Surface concrete cracks detection and segmentation using transfer learning and multi-resolution image processing

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STRUCTURES 2023年 54卷 386-398页

作者： Iraniparast, Mostafa Ranjbar, Sajad Rahai, Mohammad Nejad, Fereidoon Moghadas Amirkabir Univ Technol Tehran Polytech Dept Civil & Environm Engn Tehran Iran Sharif Univ Technol Dept Civil & Environm Engn Tehran Iran

Surface crack detection must be precise and efficient for the structural health monitoring (SHM) of concrete structures. The traditional techniques of crack detection entail human visual examination. These procedures are labor-intensive, expensive, and inefficient. In order to forestall potentially hazardous circumstances caused by concrete surfaces that have deteriorated, it is vital to have an autonomous concrete crack inspection system. crack detection and segmentation are the two main components of automatic crack inspection. In this research, deep convolutional neural networks (DCNN) and transfer learning (TL) methods are employed for crack detection in concrete structure images, also multiresolution image analysis based on the wavelet transform is the main process in the crack segmentation. The concrete crack dataset is randomly selected from two prevalent concrete crack datasets for developing the proposed method. The results indicated that DCNN classifier models provide good performance with F1-score ranging from 94.5 to 99.6 percent. In addition, the proposed multiresolution image analysis can segment crack pixels with 95.25 percent for the F1-score and shows acceptable and stable performance regarding various segmentation performance metrics.

关键词： Concrete structures automatic crack detection Segmentation Transfer learning Multiresolution image analysis

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