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Reseach Article

Mechanical Part Surface Defect Detection using Crack Extraction Approach

by Priyanka Khandelwal, Pankaj Kumar Gautam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 18
Year of Publication: 2014
Authors: Priyanka Khandelwal, Pankaj Kumar Gautam
10.5120/17624-8383

Priyanka Khandelwal, Pankaj Kumar Gautam . Mechanical Part Surface Defect Detection using Crack Extraction Approach. International Journal of Computer Applications. 100, 18 ( August 2014), 13-17. DOI=10.5120/17624-8383

@article{ 10.5120/17624-8383,
author = { Priyanka Khandelwal, Pankaj Kumar Gautam },
title = { Mechanical Part Surface Defect Detection using Crack Extraction Approach },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 18 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number18/17624-8383/ },
doi = { 10.5120/17624-8383 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:30:17.280095+05:30
%A Priyanka Khandelwal
%A Pankaj Kumar Gautam
%T Mechanical Part Surface Defect Detection using Crack Extraction Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 18
%P 13-17
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Visual inspection constitutes an important part of quality control in manufacturing industry. The detection of defects on mechanical part surfaces is an important quality control step in the manufacturing of machine products. In this paper, we have introduced a new approach to detect surface defects with varied size, shape in mechanical parts through the use of image processing techniques. First, we apply image edge detection techniques for extracting the edges in an image by identifying pixels where intensity variation is high. Then, for extracting actual defects we reduce gray scale edge information to binary defect information using thresholding. A threshold process will generate a certain amount of noise. So, this noise will removed by a noise filtering technique using the connected component's eccentricity property. Then, based on the highlighted edges, the defect itself should become identifiable by filling the gap between two corresponding edges by comparing gray scale values. The Experimental results show that the proposed method is suitable for extracting the various defects of varying shapes and size in images.

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Index Terms

Computer Science
Information Sciences

Keywords

Defect Detection Crack Extraction Edge Detection Thresholding Connected Component Property Gray Scale values