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Related Experiment Video

Updated: Jun 9, 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

YOLO-LFPD: A Lightweight Method for Strip Surface Defect Detection.

Jianbo Lu¹, Mingrui Zhu², Kaixian Qin¹

¹Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning 530001, China.

Biomimetics (Basel, Switzerland)

|October 25, 2024

Summary

This summary is machine-generated.

This study introduces YOLO-LFPD, an improved model for strip steel surface defect detection. It enhances detection speed and accuracy, making it suitable for real-time industrial applications.

Keywords:

FasterNet YOLO lightweighting pruning surface defect recognition

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Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Strip steel surface defect recognition is crucial for industrial production.
Existing methods face challenges in feature extraction, detection speed, and dataset limitations.

Purpose of the Study:

To propose an improved lightweight model for efficient and accurate strip steel surface defect detection.
To enhance real-time monitoring capabilities in industrial settings.

Main Methods:

Developed the YOLO-LFPD (lightweight fine particle detection) model based on YOLOv5.
Integrated RepVGG module for enhanced robustness and FasterNet as the backbone for accelerated inference.
Employed pruning and a GA genetic algorithm with OTA loss function for model optimization.

Main Results:

Achieved a 48% reduction in parameters and a 13% reduction in GFLOPs.
Reduced inference time by 77% compared to the original model.
Improved accuracy by 3% to 81.2% on the NEU-DET dataset, outperforming other recent models.

Conclusions:

The YOLO-LFPD model offers significant improvements in efficiency and accuracy for strip steel defect detection.
The proposed model provides a valuable reference for lightweight, real-time defect detection systems.