Zhang Shixiong, Li Ang, Ren Jianxin, Li Xingchong
School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou, China.
Sci Rep. 2024 Nov 14;14(1):28035. doi: 10.1038/s41598-024-79549-y.
To address the need for automated sorting of synthetic diamonds based on quality in manufacturing enterprises, this study developed a dedicated dataset and an enhanced YOLOv8n model for synthetic diamonds detection and quality evaluation, named YOLOv8n-adamas. We redesigned the backbone network to improve feature extraction capabilities and introduced a dynamic detection head based on attention mechanisms to further enhance model performance. Experimental results show that on synthetic diamonds dataset, YOLOv8n-adamas achieved a 4.0% improvement in precision (P), a 2.7% increase in recall (R), and improvements of 1.5% and 1.3% in mean average precisions at 50% and 95% Intersection over Union (IoU) thresholds (mAP50 and mAP95) compared to YOLOv8. Furthermore, YOLOv8n-adamas also outperforms other commonly used, high-performing models in various metrics on this dataset, offering effective technical support for the automated quality-based sorting of synthetic diamonds.
为满足制造企业中基于质量对合成钻石进行自动分选的需求，本研究开发了一个专用数据集和一个用于合成钻石检测与质量评估的增强型YOLOv8n模型，命名为YOLOv8n - adam。我们重新设计了骨干网络以提高特征提取能力，并引入了基于注意力机制的动态检测头以进一步提升模型性能。实验结果表明，在合成钻石数据集上，与YOLOv8相比，YOLOv8n - adam的精度（P）提高了4.0%，召回率（R）提高了2.7%，在50%和95%交并比（IoU）阈值下的平均精度均值（mAP50和mAP95）分别提高了1.5%和1.3%。此外，YOLOv8n - adam在该数据集的各项指标上也优于其他常用的高性能模型，为基于质量的合成钻石自动分选提供了有效的技术支持。
