An online inspection system of surface defects for copper strip based on computer vision

2012 ◽  
Author(s):  
Fei Gao ◽  
Zheng Li ◽  
Gang Xiao ◽  
Xiaoyang Yuan ◽  
Zhenggao Han
Keyword(s):  
Computer Vision ◽  
Surface Defects ◽  
Inspection System ◽  
Copper Strip ◽  
Online Inspection

scholarly journals Smart technology measurement for surface defects inspection in die casting

2018 ◽  
Vol 19 (12) ◽  
pp. 1002-1005
Author(s):  
Sławomir Świłło ◽  
Jan Wąsik
Keyword(s):  
Surface Defects ◽  
Vision System ◽  
Die Casting ◽  
Inspection System ◽  
Manufacturing Cost ◽  
Smart Technology ◽  
Consistent Finding ◽  
Vision Inspection ◽  
Automated Method ◽  
Online Inspection

The objective of the paper is to demonstrate a measurement strategy for online inspection of surface defects in products, especially discontinuities which appear in castings after machining. The essence of the proposed online vision inspection system is to provide an automated method for obtaining and analyzing images of the inspected surfaces, to allow an unmistakable and consistent finding of defects and specifying their types. The proposed solution could improve productivity and quality in the manufacturing process especially for the automotive industry and significantly improve automatization in die casting technology. This is one of the most fundamental problems of a research studies in process design for automotive parts such as: handles of transmission systems, cylinder pistons and cylinder front faces in engine bodies, that has significant influence for the manufacturing cost. Proposed by the author an online vision inspection systems are responded to users demands for systems that are easier to use, low-cost, and flexible. Using a combination of a vision hardware and software, the proposed vision system could analyze the images, usually in a fraction of a second. Since the human visual inspection is slow and expensive, a smart technology is an alternative solution for the online inspection. The developed smart technology uses advance vision system with specially designed lighting, an advanced image processing algorithm for defect detection and their classification.

scholarly journals A Combined Image Inspection System with Discrimination of Various Kinds of Surface Defects

1995 ◽  
Vol 115 (3) ◽  
pp. 452-459
Author(s):  
Saburo Okada ◽  
Masaaki Imade ◽  
Hidekazu Miyauchi ◽  
Tetsuhiro Sumimoto ◽  
Hideki Yamamoto
Keyword(s):  
Surface Defects ◽  
Inspection System ◽  
Image Inspection

Detection and classification of surface defects of gun barrels using computer vision and machine learning

Measurement ◽  
2015 ◽  
Vol 60 ◽  
pp. 222-230 ◽  
Author(s):  
Rajalingappaa Shanmugamani ◽  
Mohammad Sadique ◽  
B. Ramamoorthy
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Surface Defects

Inspection of Surface Defects in Copper Strip Based on Machine Vision

2010 ◽  
pp. 304-312 ◽  
Author(s):  
Xue-Wu Zhang ◽  
Li-Zhong Xu ◽  
Yan-Qiong Ding ◽  
Xin-Nan Fan ◽  
Li-Ping Gu ◽  
...  
Keyword(s):  
Machine Vision ◽  
Surface Defects ◽  
Copper Strip

Computer Vision Based Technique for Surface Defect Detection of Apples

2014 ◽  
pp. 111-121
Author(s):  
C. J. Prabhakar ◽  
S. H. Mohana
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Quality Standards ◽  
Production Costs ◽  
Automatic Inspection ◽  
Segmentation Technique ◽  
Surface Defect Detection ◽  
Fruit Grading

The automatic inspection of quality in fruits is becoming of paramount importance in order to decrease production costs and increase quality standards. Computer vision techniques are used in fruit industry for fruit grading, sorting, and defect detection. In this chapter, we review recent approaches for automatic inspection of quality in fruits using computer vision techniques. Particularly, we focus on the review of advances in computer vision techniques for automatic inspection of quality of apples based on surface defects. Finally, we present our approach to estimate the defects on the surface of an apple using grow-cut and multi-threshold based segmentation technique. The experimental results show that our method effectively estimates the defects on the surface of apples significantly more effectively than color based segmentation technique.

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