A surface defect identification method based on improved threshold segmentation algorithm

For enterprises, defect detection is very important because it is related to the quality of products produced by enterprises. With the development of machine vision, accurate analysis of image data benefits defect detection. In an enterprise that produces electronic cigarettes, professional and tech...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series Vol. 1651; no. 1; pp. 12072 - 12076
Main Authors: Feng, Xinglong, Gao, Xianwen, Luo, Ling
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.11.2020
Subjects:
Algorithms
Identification methods
Image segmentation
Machine vision
Physics
Surface defects
Workpieces
ISSN:1742-6588, 1742-6596
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:For enterprises, defect detection is very important because it is related to the quality of products produced by enterprises. With the development of machine vision, accurate analysis of image data benefits defect detection. In an enterprise that produces electronic cigarettes, professional and technical personnel used to detect a defect in a workpiece by using manual testing. The defect detection rate of this method is only 95%, and the efficiency is low. We use an improved threshold segmentation method to solve this problem in this paper and we have achieved success. Compared with typical methods, the accuracy of our proposed algorithm reaches over 99% and at the same time, the detection efficiency has been improved by more than 50%. Our method also has the advantage of simplicity, practicality and low cost.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1651/1/012072