Automating Diamond Quality Control using YOLO-Based Detection Model and Its Performance Result: A Survey

Author: Ms. Diya Patel, Dr. Rajesh P. Patel, Mr. Vivek K. Shah, Mrs. Dhara Patel
Published Online: July 1, 2026
DOI: http://doi.org/10.63766/spujstmr.26.000102
Abstract
References

The diamond industry has long depended on human quality grading in accordance with the principles of the "4Cs"— color, clarity, cut, and carat. Even though effective, it is subjective, labor-intensive, and prone to in-grader inconsistency. Advances in computer vision and deep learning have presented a pathway to overcome these limitations through objective, reproducible, and scalable measurements. Pioneering work such as Image Net and convolution neural networks provided the basis for modern object detection models, and structures such as Faster R-CNN, SSD, and YOLO have been adapted to pinpoint minute details relevant to diamond grading. These machine learning-based techniques have enabled computerized detection of inclusions, surface imperfections, and minute color nuances, which are invaluable for diamond examination and classification. Over recent years, research and industrial partnerships have pushed the implementation of automated grading technologies. Studies in education showcase machine learning-based solutions for colour and clarity grading, gemstone classification, and defect identification, whereas industrial competitors like GIA and Sarine Technologies have incorporated AI into commercial grading processes. In spite of these developments, there are still major challenges, such as paucity of annotated diamond datasets, inconsistency in imaging conditions, and a lack of standardized benchmarks for testing. Prospects look toward multimodal sensing, transfer learning, and explainable AI, to foster greater trust and transparency, leading eventually to dependable, fully automated systems for diamond quality control.

Keywords: Diamond quality control, automated grading, computer vision, deep learning, gemstone classification, color grading, YOLO explainable AI.
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