Review of Industry Workpiece Classification and Defect Detection using Deep Learning

• Published in: International journal of advanced computer science & applications 2022, Vol.13 (4)
• Main Authors: Chen, Changxing, Abdullah, Azween, Kok, S. H., Tien, D. T. K.
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Deep learning; Empirical analysis; Image classification; Learning theory; Literature reviews; Machine learning; Machine vision; Object recognition; Workpieces
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2022.0130439

Object detection and classification denotes one of the most extensively-utilized machine vision applications given the high requirements put forward for object classification and defect detection with the rise of object recognition scenes. Notwithstanding, conventional image recognition processing technology encounters specific drawbacks. Its benefits and limitations were duly compared upon selecting several typical conventional image recognition techniques. Resultantly, such recognition approaches required multiple manual participation elements and extensive manpower with restricted object identification. As a branch of machine learning, deep learning has attained more optimal results in the image recognition discipline. In the classification and defect detection of industrial workpieces, over 70 literature reviews of deep learning algorithms across multiple application scenarios for classical algorithm model and network structure assessment based on the deep learning theory. Relevant network model performance was compared and analyzed based on network intricacies parallel to natural image classification. Six research gaps were found based on the reviewed algorithm pros and cons. The corresponding six research proposal in workpiece image classification was highlighted with prospects on the workpiece image classification and defect detection direction development. It provides an empirical solution for the selection of workpiece classification and defect detection deep learning model in the future.