a new classification of existing techniques for error/defect detection in image processing

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.