Machine Vision Based on Pipe Joint Surface Defect Detection and Identification

In the production process, pipe joints often have surface defects such as cracks, pits, bumps, etc., and the manual detection efficiency is low and the cost is high. This paper presents a machine vision based method for detecting surface defects of pipe joints. The preprocessing algorithm, image ini...

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Bibliographic Details
Published in:Journal of physics. Conference series 2020-08, Vol.1621 (1), p.12012
Main Authors: Zhu, Li, Bao, Xukang, Liu, Shousheng
Format: Article
Language:English
Subjects:
Algorithms
Back propagation networks
BP neural network
Classifiers
Cracks
defect segmentation
Defects
Feature extraction
Flaw detection
Image segmentation
Machine vision
Physics
Pipe joint defects
Pipe joints
Pits
Surface defects
Tube joints
Vision systems
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Description
Summary:In the production process, pipe joints often have surface defects such as cracks, pits, bumps, etc., and the manual detection efficiency is low and the cost is high. This paper presents a machine vision based method for detecting surface defects of pipe joints. The preprocessing algorithm, image initial detection algorithm, image segmentation algorithm and feature extraction algorithm of tube joint surface defect image are studied and BP neural network classifier is designed. Finally, the designed classifier was tested by 300 samples of cracks, pits and bump defects on the surface of the pipe joint. The experimental results show that the recognition rate of the classifier reaches 95.5%, which can better meet the surface of the pipe joint. Defect detection requirements.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1621/1/012012