Detection crack in image using Otsu method and multiple filtering in image processing techniques

Talab, Ahmed Mahgoub Ahmed; Huang, Zhijian; Fan, Xingyu; Liu, Haiming

doi:10.1016/j.ijleo.2015.09.147

Cited by 244 publications

(120 citation statements)

References 12 publications

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“…Talab et al [2] detected cracking defects in digital images using the Otsu method and Sobel's filtering in image processing techniques. Ebrahimkhanlou et al [18] performed a multifractal analysis of crack patterns with applications in reinforced concrete shear walls.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…A method that combines the Sobel and Otsu method has been proposed in [2]; the proposed approach has shown certain improvement in crack detection on concrete surface in comparison with the original Otsu method. However, this hybrid method cannot successfully remove noncrack objects from the digital image.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…e reason is that when cracks are developed and propagate, they tend to cause the reduction in the effective loading area which brings about the increase of stress and subsequently failure of the concrete or other structures [2]. Since there always exist constraints in reinforced concrete structures and buildings deteriorate overtime, cracking seems unavoidable and appears in all types of structures, for example, concrete wall, beam, slab, and brick walls ( Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

Hoang

2018

Advances in Civil Engineering

112

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e detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. e manual process of crack detection is painstakingly time-consuming and suffers from subjective judgments of inspectors. is study establishes an intelligent model based on image processing techniques for automatic crack recognition and analyses. In the new model, a gray intensity adjustment method, called Min-Max Gray Level Discrimination (M2GLD), is proposed to preprocess the image thresholded by the Otsu method. e goal of this gray intensity adjustment method is to meliorate the accuracy of the crack detection results. Experimental results point out that the integration of M2GLD and the Otsu method, followed by other shape analysis algorithms, can successfully detect crack defects in digital images. erefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.

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Section: Review Of Related Workmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

Hoang

2018

Advances in Civil Engineering

112

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“…To select skin sample pixels from a skin map, this paper exploits the histogram binarization proposed by Otsu [18][19][20][21]. Otsu method statistically selects an optimal …”

Section: Hand Region Segmentationmentioning

confidence: 99%

User-Friendly Interface Control Using Segmented Skin Areas

Jang¹,

Lee²

2018

IJAST

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“…For example, Talab et al(2016) utilized Otsu algorithm (Otsu, 1979) for the determination of threshold for crack detection, but due to noise it is suggest to apply Sobel filter to remove noise effect in advance. Zhang et al(2014) perform noise removal as well, but conduct machine learning for crack detection.…”

Section: Introductionmentioning

confidence: 99%

Bridge Crack Detection Using Multi-Rotary Uav and Object-Base Image Analysis

Rau

Hsiao

Jhan

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Bridge is an important infrastructure for human life. Thus, the bridge safety monitoring and maintaining is an important issue to the government. Conventionally, bridge inspection were conducted by human in-situ visual examination. This procedure sometimes require under bridge inspection vehicle or climbing under the bridge personally. Thus, its cost and risk is high as well as labor intensive and time consuming. Particularly, its documentation procedure is subjective without 3D spatial information. In order cope with these challenges, this paper propose the use of a multi-rotary UAV that equipped with a SONY A7r2 high resolution digital camera, 50 mm fixed focus length lens, 135 degrees up-down rotating gimbal. The target bridge contains three spans with a total of 60 meters long, 20 meters width and 8 meters height above the water level. In the end, we took about 10,000 images, but some of them were acquired by hand held method taken on the ground using a pole with 2-8 meters long. Those images were processed by Agisoft PhotoscanPro to obtain exterior and interior orientation parameters. A local coordinate system was defined by using 12 ground control points measured by a total station. After triangulation and camera self-calibration, the RMS of control points is less than 3 cm. A 3D CAD model that describe the bridge surface geometry was manually measured by PhotoscanPro. They were composed of planar polygons and will be used for searching related UAV images. Additionally, a photorealistic 3D model can be produced for 3D visualization. In order to detect cracks on the bridge surface, we utilize object-based image analysis (OBIA) technique to segment the image into objects. Later, we derive several object features, such as density, area/bounding box ratio, length/width ratio, length, etc. Then, we can setup a classification rule set to distinguish cracks. Further, we apply semi-global-matching (SGM) to obtain 3D crack information and based on image scale we can calculate the width of a crack object. For spalling volume calculation, we also apply SGM to obtain dense surface geometry. Assuming the background is a planar surface, we can fit a planar function and convert the surface geometry into a DSM. Thus, for spalling area its height will be lower than the plane and its value will be negative. We can thus apply several image processing technique to segment the spalling area and calculate the spalling volume as well. For bridge inspection and UAV image management within a laboratory, we develop a graphic user interface. The major functions include crack auto-detection using OBIA, crack editing, i.e. delete and add cracks, crack attributing, 3D crack visualization, spalling area/volume calculation, bridge defects documentation, etc.

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Detection crack in image using Otsu method and multiple filtering in image processing techniques

Cited by 244 publications

References 12 publications

Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

User-Friendly Interface Control Using Segmented Skin Areas

Bridge Crack Detection Using Multi-Rotary Uav and Object-Base Image Analysis

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