scholarly journals Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2052 ◽  
Author(s):  
Hyunjun Kim ◽  
Junhwa Lee ◽  
Eunjong Ahn ◽  
Soojin Cho ◽  
Myoungsu Shin ◽  
...  
Keyword(s):  
Image Processing ◽  
Crack Identification ◽  
Concrete Crack

scholarly journals Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2021 ◽  
Author(s):  
Ronghua Fu ◽  
Hao Xu ◽  
Zijian Wang ◽  
Lei Shen ◽  
Maosen Cao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Concrete Surface ◽  
Identification Accuracy ◽  
Fine Tuning ◽  
Crack Identification ◽  
Image Preprocessing ◽  
Promising Tool ◽  
Concrete Crack ◽  
Surface Images

Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated.

scholarly journals Crack Identification Method of Steel Fiber Reinforced Concrete Based on Deep Learning: A Comparative Study and Shared Crack Database

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yang Ding ◽  
Shuang-Xi Zhou ◽  
Hai-Qiang Yuan ◽  
Yuan Pan ◽  
Jing-Liang Dong ◽  
...  
Keyword(s):  
Reinforced Concrete ◽  
Concrete Structure ◽  
Prediction Accuracy ◽  
Steel Fiber ◽  
Fiber Reinforced Concrete ◽  
Bending Test ◽  
Crack Identification ◽  
Fiber Reinforced ◽  
Steel Fiber Reinforced Concrete ◽  
Concrete Crack

As a common disease of concrete structure in engineering, cracks mainly lead to durability problems such as steel corrosion, rain erosion, and protection layer peeling, and then the building gets destroyed. In order to detect the cracks of concrete structure in time, the bending test of steel fiber reinforced concrete is carried out, and the pictures of concrete cracks are obtained. Furthermore, the crack database is expanded by the migration learning method and the crack database is shared on the Baidu online disk. Finally, a concrete crack identification model based on YOLOv4 and Mask R-CNN is established. In addition, the improved Mask R-CNN method is proposed in order to improve the prediction accuracy based on the Mask R-CNN. The results show that the average prediction accuracy of concrete crack identification is 82.60% based on the YOLO v4 method. The average prediction accuracy of concrete crack identification is 90.44% based on the Mask R-CNN method. The average prediction accuracy of concrete crack identification is 96.09% based on the improved Mask R-CNN method.

scholarly journals Construction of Concrete Surface Crack Recognition Model Based on Digital Image Processing Technology

2021 ◽  
Vol 2074 (1) ◽  
pp. 012067
Author(s):  
Yuzhong Kang ◽  
Aimin Yu ◽  
Wenquan Zeng
Keyword(s):  
Image Processing ◽  
Prestressed Concrete ◽  
Crack Width ◽  
Crack Detection ◽  
Concrete Surface ◽  
Concrete Bridges ◽  
Crack Identification ◽  
Detection Accuracy ◽  
Depth Analysis ◽  
Class B

Abstract In this paper, the bridge crack detection method based on digital images is studied. In-depth analysis and evaluation are performed on the image processing algorithms such as image graying, resolution of checkerboard corner pixel rate, filtering denoising, and edge detection, etc. The calculation and software system for bridge crack width based on videos (or images) is implemented, and 15 bridge crack images are used to verify its crack detection accuracy. The results suggest that the proposed crack identification method in this paper can be used for the crack detection of reinforced concrete bridges and class B prestressed concrete bridges properly. When the crack width is greater than 0.3 mm, the calculated crack width value based on images is very close to the measured value.

Research on Crack Detection Algorithm of Mining Car Baffle

2020 ◽  
Vol 13 ◽  
Author(s):  
Weiwei Li ◽  
Fanlei Yan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Crack Detection ◽  
Low Cost ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Crack Identification ◽  
Actual Length ◽  
Computer Vision Technology ◽  
Sobel Edge Detection

Introduction: Image processing technology is widely used for crack detection. This technology is to build a data acquisition system and use computer vision technology for image analysis. Because of its simplicity in the processing, many of the image processing detection methods were proposed. It is relatively easy to deploy and has low cost. Method: The heterogeneity of the external light usually changes the authenticity of each target in the image, which will seriously cause the experiment to fail. At this time, the image needs to be processed by the gamma transform.Based on the analysis of the characteristics of the image of the mine car baffle, this paper improves the Gamma transform, and uses the improved Gamma transform to enhance the image. Result: We can conclude that the algorithm in this paper can accurately detect crack areas with an actual width greater than 1.2 mm, and the error between the detected crack length and the actual length is between (-2, 2) mm. In practice, this error is completely acceptable. Discussion: To compare the performance of a new crack detection method with existing methods, are used. The two most well-known traditional methods, Canny and Sobel edge detection, are selected. Although the Sobel edge detection provides some crack information. The texture of the surface of the mine cart baffle detected has caused great interference to the crack identification. Conclusion: If the cracks appearing on the mine car baffle are not found in time, they often cause accidents. Therefore, effective crack detection must be performed. If manual inspection is adopted for crack detection, it will be labor-intensive and easy to miss inspection. In order to reduce the labor of crack detection of mine cars and improve the accuracy of detection, this paper, based on the detection platform built, performs preprocessing, image enhancement, and convolution operations on the collected crack images of the mine car baffle.

Algorithm research on image processing for crack identification of round wood

2020 ◽  
Author(s):  
Xiaogang Dang ◽  
Xiaofeng Bai ◽  
Xulang Chen ◽  
Lu Han ◽  
Lei Wang ◽  
...  
Keyword(s):  
Image Processing ◽  
Crack Identification ◽  
Round Wood
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