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Updated: May 10, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Published on: May 15, 2017

Research on a lightweight model for laser-cut diamond defect detection based on multi-module collaborative

Anfu Zhu¹, Qinghua Jiang², Heng Guo²

¹School of Electronic Engineering, North China University of Water Resources and Electric Power, Zhengzhou, China. zhuanfu@ncwu.edu.cn.

Scientific Reports

|May 8, 2026

Summary

This summary is machine-generated.

This study introduces FAS-YOLO, a lightweight deep learning model for detecting defects in laser-cut diamonds. It achieves high accuracy while significantly reducing computational load, enabling use on resource-restricted devices.

Keywords:

Adaptive downsampling Attention mechanism Deep learning Diamond defect detection Frequency-domain dynamic convolution

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Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Laser cutting of diamonds can introduce defects like cracks and ablation.
Accurate defect detection is vital for diamond quality and production efficiency.
Existing deep learning models are often too computationally intensive for portable inspection.

Purpose of the Study:

To develop a lightweight and efficient defect detection model for laser-cut diamonds.
To address the limitations of traditional deep learning algorithms in resource-constrained environments.
To improve the accuracy and speed of diamond defect identification.

Main Methods:

Developed FAS-YOLO, a lightweight model based on the YOLOv11n framework.
Integrated Frequency Domain Convolution (FDConv) for enhanced feature extraction.
Employed Adaptive Downsampling (ADown) to reduce parameter redundancy and SEAM attention for improved focus on defect regions.

Main Results:

FAS-YOLO achieved 92% precision, 80.4% recall, and 82.6% mAP50.
Significantly reduced model parameters (37.4%), GFLOPS (40%), and model size (34.6%) compared to YOLOv11n.
Demonstrated competitive performance with enhanced defect feature capture and background suppression.

Conclusions:

FAS-YOLO offers an efficient and accurate solution for laser-cut diamond defect detection.
The model's lightweight design makes it suitable for deployment on handheld inspection devices.
This research contributes to improving quality control in diamond processing through advanced AI.