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Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY.

Tamás Czimmermann¹, Gastone Ciuti¹, Mario Milazzo¹

¹The BioRobotics Institute of Scuola Superiore Sant'Anna and Department of Excellence in Robotics and AI of Scuola Superiore Sant'Anna, 56025 Pontedera (PISA), Italy.

Sensors (Basel, Switzerland)

|March 12, 2020

Summary

This review covers automated visual defect detection for materials like metals and textiles. It details defect classification and advanced artificial intelligence methods for accurate identification.

Keywords:

classification deep learning defect detection industry 4.0 survey

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Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Automated visual inspection is crucial for quality control in manufacturing.
Defects in materials like metals, ceramics, and textiles can be visible or palpable.
Existing methods for defect detection vary in complexity and effectiveness.

Purpose of the Study:

To provide a comprehensive review of automated visual-based defect detection approaches.
To classify different types of material defects.
To survey artificial visual processing techniques and machine learning classifiers for defect detection.

Main Methods:

Classification of defects into visible and palpable categories.
Description of artificial visual processing techniques for scene understanding.
Survey of textural defect detection methods (statistical, structural).
Review of supervised and non-supervised classifiers and deep learning for defect detection and classification.

Main Results:

A taxonomy of visible and palpable defects is presented.
Various artificial visual processing techniques are described.
State-of-the-art approaches using classifiers and deep learning are reported.

Conclusions:

Automated visual inspection offers efficient defect detection across diverse materials.
Advanced techniques, including deep learning, are advancing defect classification accuracy.
This review provides a foundational understanding of current defect detection methodologies.