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Related Experiment Video

Updated: Mar 31, 2026

$High Pressure Single Crystal Diffraction at PX^2$

High Pressure Single Crystal Diffraction at PX^2

Published on: January 16, 2017

Diamond-DETR: lightweight real-time quality evaluation algorithm for synthetic diamonds.

Xin Yan¹, Saidong Yang², Shixiong Zhang³

¹School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou, 450000, China.

Scientific Reports

|March 29, 2026

Summary

A new algorithm, Diamond-DETR, accurately detects subtle defects in synthetic diamonds. This method enhances quality grading and value assessment, especially in resource-limited settings.

Keywords:

DRBC3 block Diamond-DETR HSFPN encoder RepFasterNet block Synthetic diamond quality evaluation

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Last Updated: Mar 31, 2026

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Assessment of Boron Doped Diamond Electrode Quality and Application to In Situ Modification of Local pH by Water Electrolysis

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$Synthesis and Microdiffraction at Extreme Pressures and Temperatures$

Synthesis and Microdiffraction at Extreme Pressures and Temperatures

Published on: October 7, 2013

Area of Science:

Materials Science
Computer Vision
Artificial Intelligence

Background:

Synthetic diamond manufacturing can introduce subtle defects affecting quality and value.
Accurate defect detection is challenging due to diamond's complex geometry and varied defect scales.
Existing methods may struggle with accuracy and efficiency in resource-constrained environments.

Purpose of the Study:

To develop an advanced target detection algorithm for synthetic diamond quality evaluation.
To improve defect detection accuracy and model generalization in resource-limited scenarios.
To create a computationally efficient model for real-time industrial inspection.

Main Methods:

Proposed Diamond-DETR algorithm optimizing backbone with lightweight multi-scale feature extraction and RepFasterNet blocks.
Incorporated an encoder with a screening-feature fusion pyramid network and channel attention for multi-scale feature fusion.
Introduced a cross-stage fusion module with dilated convolutions to enhance long-range dependency perception.

Main Results:

Diamond-DETR demonstrated superior parameter efficiency, inference speed, and detection accuracy compared to RT-DETR.
The model is well-suited for deployment in resource-constrained inspection scenarios.
Achieved competitive performance on an external industrial dataset, showing cross-dataset applicability.

Conclusions:

Diamond-DETR offers an effective solution for accurate and efficient synthetic diamond defect detection.
The algorithm's performance makes it suitable for industrial applications with limited computational resources.
The study highlights the potential for AI-driven quality control in the synthetic diamond industry.