شماره ركورد كنفرانس :
5467
عنوان مقاله :
A review on machine/deep learning techniques for defect/error detection in image processing
پديدآورندگان :
Abdulzahra Saad Alsaide Haider headerabd917@gmail.com Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran , Soltanaghaei Mohammadreza soltan@khuisf.ac.ir Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran , Hussein Zayer Al-Lami Wael wael.zayer@stu.edu.iq Department of Engineering Technical, College of Missan, Missan, Iraq , Asgarnezhad Razieh r.asgarnezhad@aghigh.ac.ir Department of Computer Engineering, Aghigh Institute of Higher Education Shahinshahr, 8314678755, Isfahan, Iran
كليدواژه :
Machine Learning , Deep Learning , Defect , Error Detection
عنوان كنفرانس :
اولين كنفرانس بين المللي ايده هاي نو در مهندسي برق
چكيده فارسي :
The detection of defects is important in quality control in manufacturing. 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. Ultimately, we summarize and investigate the application of ultrasonic testing, filtering, deep learning, machine
