Repeatable software engineering experiments for comparing defect-detection techniques

1996 ◽  
Vol 1 (3) ◽  
pp. 241-277 ◽  
Author(s):  
Christopher M. Lott ◽  
H. Dieter Rombach
Keyword(s):  
Software Engineering ◽  
Defect Detection ◽  
Detection Techniques

scholarly journals A Study of Defect Detection Techniques for Metallographic Images

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5593 ◽  
Author(s):  
Wei-Hung Wu ◽  
Jen-Chun Lee ◽  
Yi-Ming Wang
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Network Architecture ◽  
Recognition Performance ◽  
Metallographic Analysis ◽  
Automatic Identification ◽  
Detection Techniques ◽  
Multi Scale ◽  
Modified Method ◽  
Metallographic Images

Metallography is the study of the structure of metals and alloys. Metallographic analysis can be regarded as a detection tool to assist in identifying a metal or alloy, to evaluate whether an alloy is processed correctly, to inspect multiple phases within a material, to locate and characterize imperfections such as voids or impurities, or to find the damaged areas of metallographic images. However, the defect detection of metallography is evaluated by human experts, and its automatic identification is still a challenge in almost every real solution. Deep learning has been applied to different problems in computer vision since the proposal of AlexNet in 2012. In this study, we propose a novel convolutional neural network architecture for metallographic analysis based on a modified residual neural network (ResNet). Multi-scale ResNet (M-ResNet), the modified method, improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show that the proposed method yields an accuracy of 85.7% (mAP) in recognition performance, which is higher than existing methods. As a consequence, we propose a novel system for automatic defect detection as an application for metallographic analysis.

Defect detection techniques robust to process variation in semiconductor inspection

2019 ◽  
Vol 30 (3) ◽  
pp. 035402 ◽  
Author(s):  
Minoru Harada ◽  
Yohei Minekawa ◽  
Koji Nakamae
Keyword(s):  
Defect Detection ◽  
Process Variation ◽  
Detection Techniques

Comparing and combining software defect detection techniques

1997 ◽  
Vol 22 (6) ◽  
pp. 262-277 ◽  
Author(s):  
Murray Wood ◽  
Marc Roper ◽  
Andrew Brooks ◽  
James Miller
Keyword(s):  
Defect Detection ◽  
Detection Techniques ◽  
Software Defect
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