Open AccessMachine Vision Based on Pipe Joint Surface Defect Detection and Identification
Author(s) -
Lianqing Zhu,
Xukang Bao,
Shousheng Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1621/1/012012
Subject(s) - preprocessor , artificial intelligence , segmentation , pattern recognition (psychology) , feature extraction , computer science , joint (building) , machine vision , computer vision , classifier (uml) , image segmentation , engineering , structural engineering
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.