-
91دورية أكاديمية
المؤلفون: Yang Wu, Qingbang Han, Qilin Jin, Jian Li, Yujing Zhang
المصدر: Applied Sciences, Vol 13, Iss 19, p 10583 (2023)
مصطلحات موضوعية: crack detection, instance segmentation, underwater crack detection, YOLOv8, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Remotely operated vehicles (ROVs) and unmanned aerial vehicles (UAVs) provide a solution for dam and bridges structural health information acquisition, but problems like effective damage-related information extraction also occur. Vision-based crack detection methods can replace traditional manual inspection and achieve fast and accurate crack detection. This paper thereby proposes a lightweight, real-time, pixel-level crack detection method based on an improved instance segmentation model. A lightweight backbone and a novel efficient prototype mask branch are proposed to decrease the complexity of the model and maintain the accuracy of the model. The proposed method attains an accuracy of 0.945 at 129 frames per second (FPS). Moreover, our model has smaller volume, lower computational requirements and is suitable for low-performance devices.
وصف الملف: electronic resource
-
92دورية أكاديمية
المؤلفون: Md. Al-Masrur Khan, Seong-Hoon Kee, Abdullah-Al Nahid
المصدر: Algorithms, Vol 16, Iss 12, p 568 (2023)
مصطلحات موضوعية: crack detection, crack quantification, Deep Learning, Dense U-net, railway sleeper, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95
الوصف: Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly accidents. Traditional methods for detecting cracks on railway sleepers are very time-consuming and lack efficiency. Therefore, nowadays, researchers are paying attention to vision-based algorithms, especially Deep Learning algorithms. In this work, we adopted the U-net for the first time for detecting cracks on a railway sleeper and proposed a modified U-net architecture named Dense U-net for segmenting the cracks. In the Dense U-net structure, we established several short connections between the encoder and decoder blocks, which enabled the architecture to obtain better pixel information flow. Thus, the model extracted the necessary information in more detail to predict the cracks. We collected images from railway sleepers, processed them in a dataset, and finally trained the model with the images. The model achieved an overall F1-score, precision, Recall, and IoU of 86.5%, 88.53%, 84.63%, and 76.31%, respectively. We compared our suggested model with the original U-net, and the results demonstrate that our model performed better than the U-net in both quantitative and qualitative results. Moreover, we considered the necessity of crack severity analysis and measured a few parameters of the cracks. The engineers must know the severity of the cracks to have an idea about the most severe locations and take the necessary steps to repair the badly affected sleepers.
وصف الملف: electronic resource
-
93دورية أكاديمية
المؤلفون: Jie Wu, Yongjin He, Chengyu Xu, Xiaoping Jia, Yule Huang, Qianru Chen, Chuyue Huang, Armin Dadras Eslamlou, Shiping Huang
المصدر: Buildings, Vol 13, Iss 12, p 3095 (2023)
مصطلحات موضوعية: structural health monitoring, crack detection, interpretability analysis, convolutional neural network, Building construction, TH1-9745
الوصف: Crack detection is an important task in bridge health monitoring, and related detection methods have gradually shifted from traditional manual methods to intelligent approaches with convolutional neural networks (CNNs) in recent years. Due to the opaque process of training and operating CNNs, if the learned features for identifying cracks in the network are not evaluated, it may lead to safety risks. In this study, to evaluate the recognition basis of different crack detection networks; several crack detection CNNs are trained using the same training conditions. Afterwards, several crack images are used to construct a dataset, which are used to interpret and analyze the trained networks and obtain the learned features for identifying cracks. Additionally, a crack identification performance criterion based on interpretability analysis is proposed. Finally, a training framework is introduced based on the issues reflected in the interpretability analysis.
وصف الملف: electronic resource
-
94دورية أكاديمية
المؤلفون: Tao Jin, Wen Zhang, Chunlai Chen, Bin Chen, Yizhou Zhuang, He Zhang
المصدر: Buildings, Vol 13, Iss 12, p 3114 (2023)
مصطلحات موضوعية: deep learning, crack detection, semantic segmentation, dataset, UAV, Building construction, TH1-9745
الوصف: Deep-learning- and unmanned aerial vehicle (UAV)-based methods facilitate structural crack detection for tall structures. However, contemporary datasets are generally established using images taken with handheld or vehicle-mounted cameras. Thus, these images might be different from those taken by UAVs in terms of resolution and lighting conditions. Considering the difficulty and complexity of establishing a crack image dataset, making full use of the current datasets can help reduce the shortage of UAV-based crack image datasets. Therefore, the performance evaluation of existing crack image datasets in training deep neural networks (DNNs) for crack detection in UAV images is essential. In this study, four DNNs were trained with different architectures based on a publicly available dataset and tested using a small UAV-based crack image dataset with 648 +pixel-wise annotated images. These DNNs were first tested using the four indices of precision, recall, mIoU, and F1, and image tests were also conducted for intuitive comparison. Moreover, a field experiment was carried out to verify the performance of the trained DNNs in detecting cracks from raw UAV structural images. The results indicate that the existing dataset can be useful to train DNNs for crack detection from UAV images; the TransUNet achieved the best performance in detecting all kinds of structural cracks.
وصف الملف: electronic resource
-
95دورية أكاديمية
المؤلفون: Ahmed Elamin, Ahmed El-Rabbany
المصدر: Sensors, Vol 23, Iss 23, p 9315 (2023)
مصطلحات موضوعية: crack detection, UAV, LiDAR, pixel-level, deep convolutional neural network, Chemical technology, TP1-1185
الوصف: Pavement surface maintenance is pivotal for road safety. There exist a number of manual, time-consuming methods to examine pavement conditions and spot distresses. More recently, alternative pavement monitoring methods have been developed, which take advantage of unmanned aerial systems (UASs). However, existing UAS-based approaches make use of either image or LiDAR data, which do not allow for exploring the complementary characteristics of the two systems. This study explores the feasibility of fusing UAS-based imaging and low-cost LiDAR data to enhance pavement crack segmentation using a deep convolutional neural network (DCNN) model. Three datasets are collected using two different UASs at varying flight heights, and two types of pavement distress are investigated, namely cracks and sealed cracks. Four different imaging/LiDAR fusing combinations are created, namely RGB, RGB + intensity, RGB + elevation, and RGB + intensity + elevation. A modified U-net with residual blocks inspired by ResNet was adopted for enhanced pavement crack segmentation. Comparative analyses were conducted against state-of-the-art networks, namely U-net and FPHBN networks, demonstrating the superiority of the developed DCNN in terms of accuracy and generalizability. Using the RGB case of the first dataset, the obtained precision, recall, and F-measure are 77.48%, 87.66%, and 82.26%, respectively. The fusion of the geometric information from the elevation layer with RGB images led to a 2% increase in recall. Fusing the intensity layer with the RGB images yielded a reduction of approximately 2%, 8%, and 5% in the precision, recall, and F-measure. This is attributed to the low spatial resolution and high point cloud noise of the used LiDAR sensor. The second dataset crack samples obtained largely similar results to those of the first dataset. In the third dataset, capturing higher-resolution LiDAR data at a lower altitude led to improved recall, indicating finer crack detail detection. This fusion, however, led to a decrease in precision due to point cloud noise, which caused misclassifications. In contrast, for the sealed crack, the addition of LiDAR data improved the sealed crack segmentation by about 4% and 7% in the second and third datasets, respectively, compared to the RGB cases.
وصف الملف: electronic resource
-
96دورية أكاديمية
المؤلفون: Li Fan, Jiancheng Zou
المصدر: Applied Sciences, Vol 13, Iss 22, p 12299 (2023)
مصطلحات موضوعية: crack detection, deep learning, dictionary learning, encoding networks, crack measurement, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Road crack detection is an important indicator of road detection. In real life, it is very meaningful work to detect road cracks. With the rapid development of science and technology, especially computer science and technology, quite a lot of methods have been applied to crack detection. Traditional detection methods rely on manual identification, which is inefficient and prone to errors. In addition, the commonly used image processing methods are affected by many factors, such as illumination, road stains, etc., so the results are unstable. In the research on pavement crack detection, many research studies mainly focus on the recognition and classification of cracks, lacking the analysis of the specific characteristics of cracks, and the feature values of cracks cannot be measured. Starting from the deep learning method in computer science and technology, this paper proposes a road crack detection technology based on deep learning. It relies on a new deep dictionary learning and encoding network DDLCN, establishes a new activation function MeLU, and adopts a new differentiable calculation method. The technology relies on the traditional Mask-RCNN algorithm and is implemented after improvement. In the comparison of evaluation indicators, the values of recall, precision, and F1-score reflect certain superiority. Experiments show that the proposed method has good implementability and performance in road crack detection and crack feature measurement.
وصف الملف: electronic resource
-
97دورية أكاديمية
المؤلفون: Jiajie Cheng, Qiunan Chen, Xiaocheng Huang
المصدر: Fractal and Fractional, Vol 7, Iss 11, p 820 (2023)
مصطلحات موضوعية: crack detection, crack segmentation, deep learning, fractal dimension estimation, low-light environment, Thermodynamics, QC310.15-319, Mathematics, QA1-939, Analysis, QA299.6-433
الوصف: The segmentation of crack detection and severity assessment in low-light environments presents a formidable challenge. To address this, we propose a novel dual encoder structure, denoted as DSD-Net, which integrates fast Fourier transform with a convolutional neural network. In this framework, we incorporate an information extraction module and an attention feature fusion module to effectively capture contextual global information and extract pertinent local features. Furthermore, we introduce a fractal dimension estimation method into the network, seamlessly integrated as an end-to-end task, augmenting the proficiency of professionals in detecting crack pathology within low-light settings. Subsequently, we curate a specialized dataset comprising instances of crack pathology in low-light conditions to facilitate the training and evaluation of the DSD-Net algorithm. Comparative experimentation attests to the commendable performance of DSD-Net in low-light environments, exhibiting superlative precision (88.5%), recall (85.3%), and F1 score (86.9%) in the detection task. Notably, DSD-Net exhibits a diminutive Model Size (35.3 MB) and elevated Frame Per Second (80.4 f/s), thereby endowing it with the potential to be seamlessly integrated into edge detection devices, thus amplifying its practical utility.
وصف الملف: electronic resource
-
98
المؤلفون: Grubîi, Victor, 1990, Johansson, Jimmy, 1978
المصدر: European Journal of Wood and Wood Products. 79(4):999-1006
مصطلحات موضوعية: Slicing veneer oak checking crack detection wood image analysis quality control, Forestry and Wood Technology, Skog och träteknik
الوصف: In this paper, a new method of measuring slicing checks for flat-sliced veneers was evaluated. The method is based on image analysis of veneer cross-sections, having highlighted the slicing checks using surface staining. The segmentation of the checks consists of global thresholding followed by some morphological operations. The outputs of the algorithm are check depth ratio and check frequency. The method was tested on flat-sliced oak (Quercus robur L. and Quercus petraea (Matt). Liebl.) veneers of different thicknesses (1.5, 2.5, 3.5 and 4.5 mm). Two distinct wood qualities and two different cutting directions (lengthwise-sliced and plain-sliced veneers) were evaluated. The algorithm performance resulted in an overall accuracy of 85% enabling an accessible method for relatively fast and accurate measurements of slicing check characteristics in lamella cross-sections. Regression analysis indicated a lack of fixed bias but the presence of proportional bias with the presented method. Check measurements indicate that by varying cutting parameters, it is possible to achieve desired check characteristics independent of slicing thickness. The semi-automated slicing check detection method could benefit further research and optimisation of the slicing process parameters and pave the way towards industrial quality control of slicing checks. The intended area of application is veneer-laminated products for interior use with the focus on veneered wood flooring.
وصف الملف: electronic
الوصول الحر: https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-101656Test
https://doi.org/10.1007/s00107-021-01672-8Test
https://lnu.diva-portal.org/smash/get/diva2:1538257/FULLTEXT01.pdfTest -
99دورية أكاديمية
المؤلفون: Usama Bin Amjad, Muhammad Shahid Siddique, Taha Shahid, Ahmed Iftikhar, Saleh M. Alogla, Jawad Ahmad
المصدر: Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol 71, Iss 5 (2023)
مصطلحات موضوعية: concrete, self-healing, bacteria, expanded perlite, crack detection, Technology, Technology (General), T1-995
الوصف: The increasing concern for the safety and sustainability of structures is calling for the development of smart self-healing materials and preventive repair methods. This research is carried out to investigate the extent of self-healing in normal-strength concrete by using Sporosarcina aquimarina – NCCP-2716 immobilized in expanded perlite (EP) as the carrier. The efficacy of crack-healing was also tested using two alternative self-healing techniques, i.e. expanded perlite (EP) concrete and direct introduction of bacteria in concrete. A bacterial solution was embedded in EP and calcium lactate pentahydrate was added as the nutrient. Experiments revealed that specimens containing EP-immobilized bacteria had the most effective crack-healing. After 28 days of healing, the values of completely healed crack widths were up to 0.78 mm, which is higher than the 0.5 mm value for specimens with the direct addition of bacteria. The specimen showed a significant self-healing phenomenon caused by substantial calcite precipitation by bacteria. The induced cracks were observed to be repaired autonomously by the calcite produced by the bacteria without any adverse effect on strength. The results of this research could provide a scientific foundation for the use of expanded perlite as a novel microbe carrier and Sporosarcina aquimarina as a potential microbe in bacteria-based self-healing concrete.
وصف الملف: electronic resource
العلاقة: https://journals.pan.pl/Content/127844/PDF/BPASTS_2023_71_5_3707.pdfTest; https://doaj.org/toc/2300-1917Test
-
100دورية أكاديمية
المصدر: Human-Centric Intelligent Systems, Vol 2, Iss 3-4, Pp 95-112 (2022)
مصطلحات موضوعية: Damage detection, Crack detection, Flood disaster, Machine learning, Convolutional neural networks, Cycle generative adversarial network (CycleGAN), Information technology, T58.5-58.64, Electronic computers. Computer science, QA75.5-76.95
الوصف: Abstract Manual investigation of damages incurred to infrastructure is a challenging process, in that it is not only labour-intensive and expensive but also inefficient and error-prone. To automate the process, a method that is based on computer vision for automatically detecting cracks from 2D images is a viable option. Amongst the different methods of deep learning that are commonly used, the convolutional neural network (CNNs) is one that provides the opportunity for end-to-end mapping/learning of image features instead of using the manual suboptimal image feature extraction. Specifically, CNNs do not require human supervision and are more suitable to be used for indoor and outdoor applications requiring image feature extraction and are less influenced by internal and external noise. Additionally, the CNN’s are also computationally efficient since they are based on special convolution layers and pooling operations that enable the full execution of CNN frameworks on several hardware devices. Keeping this in mind, we propose a deep CNN framework that is based on 10 different convolution layers along with a cycle GAN (Generative Adversarial Network) for predicting the crack segmentation pixel by pixel in an end-to-end manner. The methods proposed here include the Deeply Supervised Nets (DSN) and Fully Convolutional Networks (FCN). The use of DSN enables integrated feature supervision for each stage of convolution. Furthermore, the model has been designed intricately for learning and aggregating multi-level and multiscale features while moving from the lower to higher convolutional layers through training. Hence, the architecture in use here is unique from the ones in practice which just use the final convolution layer. In addition, to further refine the predicted results, we have used a guided filter and CRFs (Conditional Random Fields) based methods. The verification step for the proposed framework was carried out with a set of 537 images. The deep hierarchical CNN framework of 10 convolutional layers and the Guided filtering achieved high-tech and advanced performance on the acquired dataset, showing higher F-score, Recall and Precision values of 0.870, 0.861, and 0.881 respectively, as compared to the traditional methods such as SegNet, Crack-BN, and Crack-GF.
وصف الملف: electronic resource
العلاقة: https://doaj.org/toc/2667-1336Test
النص الكامل