Automated crack detection on pressed panel products using image processing (Conference Presentation)

2018 ◽  
Author(s):  
Yeseul Kong ◽  
Hoyeon Moon ◽  
Hweekwon Jung ◽  
Gyuhae Park
Keyword(s):  
Image Processing ◽  
Crack Detection ◽  
Panel Products

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 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.

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