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Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition.

Kaiyan Jin¹, Chunbin Wang²

¹Jingdezhen Ceramic University, Jingdezhen 333403, Jiangxi, China.

Computational Intelligence and Neuroscience

|September 1, 2022

Summary

This summary is machine-generated.

This study introduces an automated surface defect inspection model for decorative ceramics using Deep Learning. The model achieves 94.90% accuracy and 25 FPS, significantly improving quality inspection efficiency.

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Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Automated inspection of decorative ceramic defects is crucial for quality control.
Existing methods face challenges with noise and complex backgrounds in real-world deployment.
Improving defect detection automation enhances production efficiency and economic benefits.

Purpose of the Study:

To develop an automated surface defect inspection model for decorative ceramic workpieces.
To enhance the accuracy and speed of defect detection in ceramic manufacturing.
To address the limitations of traditional manual inspection methods.

Main Methods:

Comparison of multi-target detection algorithms and network models on public datasets.
Implementation of image preprocessing techniques, including median filtering for denoising.
Development and training of a decorative ceramic-oriented Automated Surface Defect Inspection model based on the You Only Look Once version 3 (YOLOv3) network.

Main Results:

The proposed model demonstrates strong feature extraction and inspection capabilities.
Achieved a detection accuracy of 94.90% on the test set.
Reached a detection speed of 25 frames per second, suitable for on-site requirements.

Conclusions:

The Deep Learning-based Automated Surface Defect Inspection model significantly outperforms traditional manual inspection.
The model effectively handles complex backgrounds and improves overall quality inspection efficiency.
This advancement holds significant importance for the decorative ceramics industry, boosting quality and economic returns.