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Related Experiment Video

Updated: May 6, 2026

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

FasterNet-YOLO for real-time detection of steel surface defects algorithm.

Shiwei Yu¹, Zelin Liu¹, Liang Zhang¹

¹CGN Digital Technology Co., Ltd., Shanghai, China.

|May 9, 2025

Summary

This summary is machine-generated.

This study introduces an enhanced FasterNet-YOLO model for steel surface defect detection. The improved model achieves higher accuracy and significantly faster real-time detection speeds, benefiting industrial applications.

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Area of Science:

Industrial Automation
Computer Vision
Materials Science

Background:

Steel surface defect detection is crucial for quality control in manufacturing.
Balancing accuracy and real-time performance in industrial object detection remains a challenge.

Purpose of the Study:

To develop an improved FasterNet-YOLO model for efficient and accurate steel surface defect detection.
To enhance real-time detection capabilities for industrial applications.

Main Methods:

Reconstructed the YOLOv5 backbone network using FasterNet for model lightweighting.
Improved the YOLOv5 neck network with depthwise separable convolution and integrated Swin-Transformer into the C3 module.
Utilized BiFPN for effective feature fusion to improve detection across various scales.

Main Results:

Reduced model parameters by 49.4% and GFLOPs by 57.0%.
Increased mean Average Precision (mAP) by 6.2% and Frames Per Second (FPS) by 54.1%.
Demonstrated significant improvements in both detection accuracy and speed compared to the original model.

Conclusions:

The proposed FasterNet-YOLO model effectively addresses the challenges of real-time steel surface defect detection.
The integration of FasterNet, depthwise separable convolution, Swin-Transformer, and BiFPN leads to a more efficient and accurate detection system.