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|Title: ||Application of image segmentation in inspection of welding – Practical research in MATLAB|
|Authors: ||Shen, Jiannan|
|Department: ||Högskolan i Borås/Institutionen Handels- och IT-högskolan|
|Issue Date: ||11-Oct-2012|
|Series/Report no.: ||Magisteruppsats|
|Programme: ||Magisterutbildning i informatik|
|Publisher: ||University of Borås/School of Business and IT|
|Media type: ||text|
|Keywords: ||image segmentation|
|Abstract: ||As one of main methods in modern steel production, welding plays a very important role in our national economy, which has been widely applied in many fields such as aviation, petroleum, chemicals, electricity, railways and so on. The craft of welding can be improved in terms of welding tools, welding technology and welding inspection. However, so far welding inspection has been a very complicated problem. Therefore, it is very important to effectively detect internal welding defects in the welded-structure part and it is worth to furtherly studying and researching.
In this paper, the main task is research about the application of image segmentation in welding inspection. It is introduced that the image enhancement techniques and image segmentation techniques including image conversion, noise removal as well as threshold, clustering, edge detection and region extraction. Based on the MATLAB platform, it focuses on the application of image segmentation in ray detection of steeled-structure, found out the application situation of three different image segmentation method such as threshold, clustering and edge detection.
Application of image segmentation is more competitive than image enhancement because that:
1. Gray-scale based FCM clustering of image segmentation performs well, which can exposure pixels in terms of grey value level so as that it can show hierarchical position of related defects by grey value.
2. Canny detection speeds also fast and performs well, that gives enough detail information around edges and defects with smooth lines.
3. Image enhancement only could improve image quality including clarity and contrast, which can’t give other helpful information to detect welding defects.
This paper comes from the actual needs of the industrial work and it proves to be practical at some extent. Moreover, it also demonstrates the next improvement direction including identification of welding defects based on the neural networks, and improved clustering algorithm based on the genetic ideas.|
|Appears in Collections:||Magisteruppsatser (Informatics)|
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