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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification.

Yang Liu¹, Yachao Yuan², Cristhian Balta³

¹Bremen Institute for Mechanical Engineering-bime, University of Bremen, 28359 Bremen, Germany.

Materials (Basel, Switzerland)

|October 21, 2020

Summary

This study introduces a novel Concurrent Convolutional Neural Network (ConCNN) for efficient steel surface defect classification. The ConCNN achieves high accuracy with low latency, addressing limitations of current computer vision methods.

Keywords:

classification accuracy convolutional neural networks latency multiple image scales surface defect classification

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Area of Science:

Computer Vision
Machine Learning
Materials Science

Background:

Automatic surface defect inspection is vital for industries, but current computer vision methods require extensive training data and suffer from high latency.
Existing approaches often achieve high accuracy at the cost of speed, hindering real-time applications.

Purpose of the Study:

To develop a light-weighted and efficient deep learning model for real-time steel surface defect classification.
To overcome the challenges of large dataset requirements and high latency in current defect detection systems.

Main Methods:

A novel Concurrent Convolutional Neural Network (ConCNN) architecture was designed, incorporating multi-scale image processing.
The ConCNN model was trained and evaluated using the NEU-CLS dataset for steel surface defect classification.

Main Results:

The ConCNN model achieved a high classification accuracy of 98.89% on the NEU-CLS dataset.
The proposed method demonstrated significantly lower latency, around 5.58 ms, making it suitable for real-time applications.
The ConCNN required a reduced training cost compared to existing state-of-the-art approaches.

Conclusions:

The Concurrent Convolutional Neural Network (ConCNN) offers a superior solution for real-time steel surface defect classification.
ConCNN effectively balances high accuracy, low latency, and reduced training data requirements, outperforming current methods.