Crack detection using fusion features‐based broad learning system and image processing

2021 ◽  
Author(s):  
Yang Zhang ◽  
Ka‐Veng Yuen
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Learning System ◽  
Fusion Features

scholarly journals Modified Crack Detection of Sewer Conduit with Low-Resolution Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2263
Author(s):  
Byung Jik Son ◽  
Taejun Cho
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Threshold Value ◽  
Optimal Threshold ◽  
Optimal Parameters ◽  
Low Resolution ◽  
The Subject ◽  
Low Resolution Images

Imaging devices of less than 300,000 pixels are mostly used for sewage conduit exploration due to the petty nature of the survey industry in Korea. Particularly, devices of less than 100,000 pixels are still widely used, and the environment for image processing is very dim. Since the sewage conduit images covered in this study have a very low resolution (240 × 320 = 76,800 pixels), it is very difficult to detect cracks. Because most of the resolutions of the sewer conduit images are very low in Korea, this problem of low resolution was selected as the subject of this study. Cracks were detected through a total of six steps of improving the crack in Step 2, finding the optimal threshold value in Step 3, and applying an algorithm to detect cracks in Step 5. Cracks were effectively detected by the optimal parameters in Steps 2 and 3 and the user algorithm in Step 5. Despite the very low resolution, the cracked images showed a 96.4% accuracy of detection, and the non-cracked images showed 94.5% accuracy. Moreover, the analysis was excellent in quality. It is believed that the findings of this study can be effectively used for crack detection with low-resolution images.

Crack Detection on Metal Surfaces Using Image Processing Techniques

2021 ◽  
Author(s):  
Megharaj Sonawane ◽  
Aditya Borse ◽  
Hrishikesh Sonawane ◽  
Aashish Mali ◽  
Prachi Rajarapollu
Keyword(s):  
Image Processing ◽  
Metal Surfaces ◽  
Crack Detection ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Cement Pavement Surface Crack Detection Based on Image Processing

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hongwei Lei ◽  
Jianlian Cheng ◽  
Qi Xu
Keyword(s):  
Image Processing ◽  
Laplace Operator ◽  
Image Recognition ◽  
Surface Crack ◽  
Iteration Method ◽  
Crack Detection ◽  
Binary Image ◽  
Threshold Method ◽  
Otsu Method ◽  
Pavement Crack Detection

This article introduces the application of image recognition technology in cement pavement crack detection and put forward to method for determining threshold about grayscale stretching. the algorithm is designed about binarization which has a self-adaptive characteristic. After the image is preprocessed, we apply 2D Wavelet and Laplace operator to process the image. According to the characteristic of pixel of gray image, an algorithm designed on binarization for Binary image. The feasibility of this method can be verified the image processed by comparing with the results of three algorithms: Otsu method, iteration method and fixed threshold method.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Structural Safety ◽  
Building Structures ◽  
Adjustment Method ◽  
Building Management ◽  
Otsu Method ◽  
Gray Intensity

The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is painstakingly time-consuming and suffers from subjective judgments of inspectors. This 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. The 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. Therefore, the constructed model can be a useful tool for building management agencies and construction engineers in the task of structure maintenance.

scholarly journals Development of Image Processing for Crack Detection on Concrete Structures through Terrestrial Laser Scanning Associated with the Octree Structure

2018 ◽  
Vol 8 (12) ◽  
pp. 2373 ◽  
Author(s):  
Soojin Cho ◽  
Seunghee Park ◽  
Gichun Cha ◽  
Taekeun Oh
Keyword(s):  
Image Processing ◽  
Laser Scanning ◽  
Crack Detection ◽  
Surface Condition ◽  
Terrestrial Laser Scanning ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Morphological Operations ◽  
Cloud Data ◽  
Remote Sensing Technique

Terrestrial laser scanning (TLS) provides a rapid remote sensing technique to model 3D objects but can also be used to assess the surface condition of structures. In this study, an effective image processing technique is proposed for crack detection on images extracted from the octree structure of TLS data. To efficiently utilize TLS for the surface condition assessment of large structures, a process was constructed to compress the original scanned data based on the octree structure. The point cloud data obtained by TLS was converted into voxel data, and further converted into an octree data structure, which significantly reduced the data size but minimized the loss of resolution to detect cracks on the surface. The compressed data was then used to detect cracks on the surface using a combination of image processing algorithms. The crack detection procedure involved the following main steps: (1) classification of an image into three categories (i.e., background, structural joints and sediments, and surface) using K-means clustering according to color similarity, (2) deletion of non-crack parts on the surface using improved subtraction combined with median filtering and K-means clustering results, (3) detection of major crack objects on the surface based on Otsu’s binarization method, and (4) highlighting crack objects by morphological operations. The proposed technique was validated on a spillway wall of a concrete dam structure in South Korea. The scanned data was compressed up to 50% of the original scanned data, while showing good performance in detecting cracks with various shapes.

Improvement of Crack Detection Probability by Using Magnetic Camera and Image Processing

2007 ◽  
Vol 353-358 ◽  
pp. 2375-2378 ◽  
Author(s):  
Se Ho Choi ◽  
Ji Seong Hwang ◽  
Jong Woo Jun ◽  
Jin Yi Lee ◽  
Cheol Woong Kim
Keyword(s):  
Image Processing ◽  
Detection Probability ◽  
Crack Detection ◽  
Mechanical Vibration ◽  
Processing Method ◽  
Magnetic Sensors ◽  
Magnetic Lens ◽  
Analog To Digital ◽  
Lift Off ◽  
Magnetic Camera

Magnetic camera consists of magnetic source, arrayed small magnetic sensors, magnetic lens, analog-to-digital converter and interface, computer and monitor. The quantitative magnetic field around crack and its processed results could be obtained by using magnetic camera, and the crack could be inspected and evaluated quantitatively. In addition, the magnetic camera has to uphold with large lift-off to protect sensors from weak environment such as high temperature and mechanical vibration. However, the sensor sensitivity would be decreased when the lift-off was increased. Correspondingly, the improved techniques are necessary for increasing sensitivity of magnetic camera and probability of crack detection at the large lift-off. This paper proposes an image processing method, which separates a global full scale to the several regions and repeats shadings in each region, to increase a crack detection probability in the magnetic camera images such as ∂B/∂x and ∂2B/∂x∂y.

Crack detection on unwashed eggs using image processing

2017 ◽  
Vol 209 ◽  
pp. 76-82 ◽  
Author(s):  
Jetsadaporn Priyadumkol ◽  
Chawalit Kittichaikarn ◽  
Somying Thainimit
Keyword(s):  
Image Processing ◽  
Crack Detection

Rapid and non-invasive surface crack detection for pressed-panel products based on online image processing

2019 ◽  
Vol 18 (5-6) ◽  
pp. 1928-1942 ◽  
Author(s):  
Hwee Kwon Jung ◽  
Gyuhae Park
Keyword(s):  
Image Processing ◽  
Manufacturing Process ◽  
Crack Detection ◽  
Experimental Results ◽  
Inspection System ◽  
Camera System ◽  
Non Invasive ◽  
Panel Products ◽  
Traditional Approaches ◽  
Detection And Localization

Crack detection during the manufacturing process of pressed-panel products is an important aspect of quality management. Traditional approaches for crack detection of those products are subjective and expensive because they are usually performed by experienced human inspectors. Therefore, the development and implementation of an automated and accurate inspection system is required for the manufacturing process. In this article, a crack detection technique based on image processing is proposed that utilizes the images of panel products captured by a regular camera system. First, the binary panel object image is extracted from various backgrounds after considering the color factor. Edge lines are then generated from a binary image using a percolation process. Finally, crack detection and localization is performed with a unique edge-line evaluation. In order to demonstrate the capability of the proposed technique, lab-scale experiments were carried out with a thin aluminum plate. In addition, a test was performed with the panel images acquired at an actual press line. Experimental results show that the proposed technique could effectively detect panel cracks at an improved rate and speed. The experimental results also demonstrate that the proposed technique could be an extension of structural health monitoring frameworks into a new manufacturing application.

scholarly journals Research on Crack Detection Algorithm of the Concrete Bridge Based on Image Processing

2019 ◽  
Vol 154 ◽  
pp. 610-616 ◽  
Author(s):  
Yun Wang ◽  
Ju Yong Zhang ◽  
Jing Xin Liu ◽  
Yin Zhang ◽  
Zhi Ping Chen ◽  
...  
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Detection Algorithm ◽  
Concrete Bridge
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