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Updated: Aug 15, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

DSTEELNet: A Real-Time Parallel Dilated CNN with Atrous Spatial Pyramid Pooling for Detecting and Classifying Defects

Khaled R Ahmed¹

¹School of Computing, Southern Illinois University, Carbondale, IL 62901, USA.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

This study introduces DSTEELNet, a novel deep learning model for automatic steel surface defect detection. DSTEELNet significantly improves accuracy and speed in identifying defects, enhancing quality control in the steel industry.

Keywords:

computer vision convolution neural network defect classification defect detection parallel processing

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Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

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Published on: November 1, 2018

Area of Science:

Materials Science
Computer Science
Artificial Intelligence

Background:

Automated defect detection is crucial for quality control in steel manufacturing.
Existing methods often face challenges in accuracy and processing time.

Purpose of the Study:

To develop an advanced deep learning model, DSTEELNet, for improved steel surface defect detection.
To enhance both the accuracy and efficiency of defect identification in steel strips.

Main Methods:

Proposed and developed the DSTEELNet convolutional neural network (CNN) architecture.
Utilized parallel stacks of convolution blocks with atrous spatial pyramid pooling.
Employed varying dilation rates to expand receptive fields and maintain feature resolution.

Main Results:

Achieved 97% mean Average Precision (mAP) on the GNEU and Severstal datasets.
Demonstrated defect detection in a single image within 23 milliseconds.
Showcased performance improvements with different DSTEELNet configurations.

Conclusions:

DSTEELNet offers a significant advancement in automatic steel defect inspection.
The model effectively balances high accuracy with rapid processing times.
This technology has the potential to revolutionize quality assurance in the steel industry.