Surface Defects Inspection System Based on Machine Vision

Author

Deng, Xiaoyan ; Ye, Xiaojuan ; Fang, Jinsheng ; Lin, Chun ; Wang, Lei

Author_Institution

Dept. of Mech. & Electr. Eng., Xiamen Univ., Xiamen, China

fYear

2010

fDate

25-27 June 2010

Firstpage

2205

Lastpage

2208

Abstract

A machine vision based tinplate surface inspection system was developed. The system was composed of two parallel line scan CCD cameras, a special designed wide field illumination, which can overcome the vibration of tinplate, and a software based on SOM (Self-Organizing Feature Map) neural network. The images of tinplate were captured by cameras. All kinds of defects candidates such as pinholes, scallops, dust and scratches were found out, and their features can be extracted and selected from images. These candidates were distinguished by the SOM neural network to find out real defects. The inspection speed reached up to 1.4 m/s, and the resolution was 0.1 mm, and recognition rate was 95.45%.

Keywords

CCD image sensors; computer vision; feature extraction; image resolution; inspection; metallurgical industries; object recognition; plates (structures); production engineering computing; self-organising feature maps; tin; dust; feature extraction; feature selection; image resolution; machine vision; neural network; object recognition; parallel line scan CCD camera; pinholes; scallops; scratches; self-organizing feature map; surface defect inspection system; tinplate image capture; tinplate surface inspection system; tinplate vibration; wide field illumination; Artificial neural networks; Cameras; Feature extraction; Inspection; Lighting; Machine vision; Neurons; SOM neural network; machine vision; tinplate;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Control Engineering (ICECE), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-6880-5

Type

conf

DOI

10.1109/iCECE.2010.543

Filename

5631800