英国-YOLOv10:基于深度学习的手术仪器检测
Li Zhang1, Guanqun Guo1, Wenjie Wang1
1School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China.
概括
英国-YOLOv10通过使用新的注意力和KAN卷积模块,提高了用于机器人辅助手术的手术仪器检测. 这种先进的框架实现了卓越的准确性和实时性能,提高了临床适用性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 医疗成像医学成像
背景情况:
- 精确的手术仪器检测对于机器人辅助手术至关重要.
- 目前的方法在准确性和实时性能方面扎,阻碍了临床使用.
研究的目的:
- 引入UK-YOLOv10,一个用于增强手术仪器检测的新型框架.
- 为了提高机器人辅助手术程序的准确性和实时能力.
主要方法:
- 整合了统一融合注意模块 (UFAM) 以实现优越的多尺度特征表示.
- 实现C2fKAN模块与KAN卷积,以提高分类准确性和训练速度.
主要成果:
- 英国YOLOv10在M2CAI16-Tool-Locations数据集上实现了96.7%的检测准确度.
- 它的表现优于YOLOv10,其mAP@0.5 (96.4%) 和mAP@0.5:0.95 (0.605) 均较高.
- 在COCO2017数据集上表现出强大的概括性,mAP@0.5:0.95为0.386.
结论:
- 英国YOLOv10显著提升了手术仪器检测.
- 该框架显示了增强机器人辅助手术应用的重大前景.
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