A New Classification of Existing Techniques for Error/Defect Detection in Image Processing
Subject Areas : Majlesi Journal of Telecommunication Devices
Haider Abdulzahra Saad Alsaide
1
,
Mohammad Reza Soltanaghaei
2
,
Wael Hussein Zayer Al-Lami
3
,
Razieh Asgarnezhad
4
1 -
2 -
3 -
4 -
Keywords: Machine Learning, Deep Learning, Defect Detection,
Abstract :
The detection of defects is important in quality control in manufacturing. These defects raise the costs incurred by enterprises, compress the service life of simulated products, and result in the expansive destruction of resources, thereby significantly harming people and their safety. Defect detection and classification need to be feasted as unique problems associated with the field of artificial vision. We categorize the defects like electronic components, pipes, welded parts, textile materials, etc. We express artificial visual processing techniques aimed at comprehending the charged picture in a mathematical/analytical manner. Recent mainstream and deep-learning techniques in defect detection are studied with their features, stability, and weaknesses explained. We resume with a survey of textural defect detection based on statistical, structural, and other methods. We investigate the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies utilized for defect detection to offer a new classification. In addition, high precision, high positioning, fast detection, and small objects through examination are the biggest challenges in applying quality detection.
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[4] X. Xie, "A review of recent advances in surface defect detection using texture analysis techniques," ELCVIA: electronic letters on computer vision and image analysis, pp. 1-22, 2008.
[5] H. Y. Ngan, G. K. Pang, and N. H. Yung, "Automated fabric defect detection—A review," Image and vision computing, vol. 29, pp. 442-458, 2011.
[6] P. Mahajan, S. Kolhe, and P. Patil, "A review of automatic fabric defect detection techniques," Advances in Computational Research, vol. 1, pp. 18-29, 2009.
[7] I. J. Aldave, P. V. Bosom, L. V. González, I. L. De Santiago, B. Vollheim, L. Krausz, et al., "Review of thermal imaging systems in composite defect detection," Infrared Physics & Technology, vol. 61, pp. 167-175, 2013.
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