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DSTEELNet: A Real-Time Parallel Dilated CNN with Atrous Spatial Pyramid Pooling for Detecting and Classifying Defects
1School of Computing, Southern Illinois University, Carbondale, IL 62901, USA.
Sensors (Basel, Switzerland)
|January 8, 2023
Summary
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.
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.
