The Experts below are selected from a list of 12720 Experts worldwide ranked by ideXlab platform
Matthew J. Thurley - One of the best experts on this subject based on the ideXlab platform.
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Morphology-based Crack Detection for steel slabs
IEEE Journal on Selected Topics in Signal Processing, 2012Co-Authors: Anders Landstrom, Matthew J. ThurleyAbstract:Continuous casting is a highly efficient process used to produce most of the world steel production tonnage, but can cause Cracks in the semi-finished steel product output. These Cracks may cause problems further down the production chain, and detecting them early in the process would avoid unnecessary and costly processing of the defective goods. In order for a Crack Detection system to be accepted in industry, however, false Detection of Cracks in non-defective goods must be avoided. This is further complicated by the presence of scales; a brittle, often Cracked, top layer originating from the casting process. We present an approach for an automated on-line Crack Detection system, based on 3D profile data of steel slab surfaces, utilizing morphological image processing and statistical classification by logistic regression. The initial segmentation successfully extracts 80% of the Crack length present in the data, while discarding most potential pseudo-defects (non-defect surface features similar to defects). The subsequent statistical classification individually has a Crack Detection accuracy of over 80% (with respect to total segmented Crack length), while discarding all remaining manually identified pseudo-defects. Taking more ambiguous regions into account gives a worst-case false classification of 131 mm within the 30 600 mm long sequence of 150 mm wide regions used as validation data. The combined system successfully identifies over 70% of the manually identified (unambiguous) Crack length, while missing only a few Crack regions containing short Crack segments. The results provide proof-of-concept for a fully automated Crack Detection system based on the presented method.
Shuji Hashimoto - One of the best experts on this subject based on the ideXlab platform.
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an efficient Crack Detection method using percolation based image processing
Conference on Industrial Electronics and Applications, 2008Co-Authors: Tomoyuki Yamaguchi, Shingo Nakamura, Shuji HashimotoAbstract:Crack Detection on concrete surfaces is the most popular subject in the inspection of the concrete structures. The conventional method of Crack Detection is performed by experienced human inspectors by sketching the Crack patterns manually. Some automated Crack Detection techniques utilizing image processing have been proposed. Although most of the image-based approaches pay attention to the accuracy of the Crack Detection results, the computation time is also important for practical use, because the size of the digital image reaches 10-mega pixels. In this paper, we introduce an efficient and high-speed method for Crack Detection employing percolation-based image processing. To reduce the computation time, we consult the ideas of the sequential similarity Detection algorithm and active search (SSDA). According to the concept of SSDA, the percolation process is terminated by calculating the circularity midway through the processing. Moreover, percolation processing can be skipped for the next pixel depending on the circularity of neighboring pixels. The experimental result shows that the proposed approach is efficient in reducing the computation cost while preserving the accuracy of Crack Detection result.
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Improved percolation-based method for Crack Detection in concrete surface images
2008 19th International Conference on Pattern Recognition, 2008Co-Authors: Tomoyuki Yamaguchi, Shuji HashimotoAbstract:This paper presents a highly accurate and efficient method for Crack Detection using percolation-based image processing. The Detection of Cracks in concrete surfaces during the maintenance and diagnosis of concrete structures is important to ensure the safety of these structures. Recently, the image-based Crack Detection method has attracted considerable attention due to its low cost and objectivity. However, there are several problems in the practical application of image processing for Crack Detection since real concrete surface images have noises such as concrete blebs, stains, and shadings of several sizes. In order to resolve these problems, our proposed method focuses on the number of pixels in a Crack and the connectivity of the pixels. Our method employs a percolation model for Crack Detection in order to consider the features of the Cracks. Through experiments using real concrete surface images, we demonstrate the accuracy and efficiency of our method.
Navneet Garg - One of the best experts on this subject based on the ideXlab platform.
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Piezoelectric Active Sensing System for Crack Detection in Concrete Structures
2020Co-Authors: Chen Zhang, Xun Yu, Lee Alexander, Rajesh Rajamani, Ye Zhang, Ahmadi, Navneet GargAbstract:This paper presents an active sensing system for concrete Crack Detection that is based on the energy diffusivity method. In this approach, the energy diffusivity from the piezoelectric actuator to the sensor is analyzed and used to characterize the structural integrity of the pavement. Experiments are carried out to evaluate the effect of this approach in Crack Detection. In addition, detectable range of this system is studied by testing the energy diffusivity with cuttings at different angles and different distances. By observing and analyzing the energy diffusivity change of the sensor responses, Cracks in the concrete specimen can be detected. The detectable range of this system is also discussed in detail. This Crack Detection system can be used in highway and airport pavement slabs for pavement health monitoring applications.
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Piezoelectric active sensing system for Crack Detection in concrete structure
Journal of Civil Structural Health Monitoring, 2016Co-Authors: Chen Zhang, Xun Yu, Lee Alexander, Rajesh Rajamani, Ye Zhang, Navneet GargAbstract:This paper presents an active piezoelectric sensing system for concrete Crack Detection that is based on the energy diffusivity method. The feasibility of using the energy diffusivity of ultrasound to characterize the structural integrity of a pavement is first analyzed. Experiments are then carried out to evaluate the performance of this approach to Crack Detection. In addition, the detectable range of this system is studied by testing it with cuttings at different angles and different distances between sensor and actuator. Results show that by analyzing the energy diffusivity density of the sensor responses, Cracks in the concrete specimen can be detected. This Crack Detection system can be used in highway and airport pavement slabs for pavement health monitoring applications.
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Piezoelectric active sensing system for Crack Detection in concrete structure
Journal of Civil Structural Health Monitoring, 2016Co-Authors: Chen Zhang, Xun Yu, Lee Alexander, Rajesh Rajamani, Yunsheng Zhang, Navneet GargAbstract:© 2016, Springer-Verlag.This paper presents an active piezoelectric sensing system for concrete Crack Detection that is based on the energy diffusivity method. The feasibility of using the energy diffusivity of ultrasound to characterize the structural integrity of a pavement is first analyzed. Experiments are then carried out to evaluate the performance of this approach to Crack Detection. In addition, the detectable range of this system is studied by testing it with cuttings at different angles and different distances between sensor and actuator. Results show that by analyzing the energy diffusivity density of the sensor responses, Cracks in the concrete specimen can be detected. This Crack Detection system can be used in highway and airport pavement slabs for pavement health monitoring applications.
Shirley J Dyke - One of the best experts on this subject based on the ideXlab platform.
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Vision-Based Automated Crack Detection for Bridge Inspection
Computer-Aided Civil and Infrastructure Engineering, 2015Co-Authors: Chul Min Yeum, Shirley J DykeAbstract:Crack Detection
Tomoyuki Yamaguchi - One of the best experts on this subject based on the ideXlab platform.
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ICPR - Improved percolation-based method for Crack Detection in concrete surface images
2008 19th International Conference on Pattern Recognition, 2008Co-Authors: Tomoyuki Yamaguchi, S. HashimotoAbstract:This paper presents a highly accurate and efficient method for Crack Detection using percolation-based image processing. The Detection of Cracks in concrete surfaces during the maintenance and diagnosis of concrete structures is important to ensure the safety of these structures. Recently, the image-based Crack Detection method has attracted considerable attention due to its low cost and objectivity. However, there are several problems in the practical application of image processing for Crack Detection since real concrete surface images have noises such as concrete blebs, stains, and shadings of several sizes. In order to resolve these problems, our proposed method focuses on the number of pixels in a Crack and the connectivity of the pixels. Our method employs a percolation model for Crack Detection in order to consider the features of the Cracks. Through experiments using real concrete surface images, we demonstrate the accuracy and efficiency of our method.
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an efficient Crack Detection method using percolation based image processing
Conference on Industrial Electronics and Applications, 2008Co-Authors: Tomoyuki Yamaguchi, Shingo Nakamura, Shuji HashimotoAbstract:Crack Detection on concrete surfaces is the most popular subject in the inspection of the concrete structures. The conventional method of Crack Detection is performed by experienced human inspectors by sketching the Crack patterns manually. Some automated Crack Detection techniques utilizing image processing have been proposed. Although most of the image-based approaches pay attention to the accuracy of the Crack Detection results, the computation time is also important for practical use, because the size of the digital image reaches 10-mega pixels. In this paper, we introduce an efficient and high-speed method for Crack Detection employing percolation-based image processing. To reduce the computation time, we consult the ideas of the sequential similarity Detection algorithm and active search (SSDA). According to the concept of SSDA, the percolation process is terminated by calculating the circularity midway through the processing. Moreover, percolation processing can be skipped for the next pixel depending on the circularity of neighboring pixels. The experimental result shows that the proposed approach is efficient in reducing the computation cost while preserving the accuracy of Crack Detection result.
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Improved percolation-based method for Crack Detection in concrete surface images
2008 19th International Conference on Pattern Recognition, 2008Co-Authors: Tomoyuki Yamaguchi, Shuji HashimotoAbstract:This paper presents a highly accurate and efficient method for Crack Detection using percolation-based image processing. The Detection of Cracks in concrete surfaces during the maintenance and diagnosis of concrete structures is important to ensure the safety of these structures. Recently, the image-based Crack Detection method has attracted considerable attention due to its low cost and objectivity. However, there are several problems in the practical application of image processing for Crack Detection since real concrete surface images have noises such as concrete blebs, stains, and shadings of several sizes. In order to resolve these problems, our proposed method focuses on the number of pixels in a Crack and the connectivity of the pixels. Our method employs a percolation model for Crack Detection in order to consider the features of the Cracks. Through experiments using real concrete surface images, we demonstrate the accuracy and efficiency of our method.
