Title :
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
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;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
DOI :
10.1109/iCECE.2010.543
