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增强YOLO用于腹腔镜工具检测:新增数据增强和结构修改,解决错误检测的分叉目标

Yuzhang Liu¹, Yuichiro Hayashi², Masahiro Oda^2,3

¹Graduate School of Informatics, Nagoya University, Furo-Cho, Chikusa-ku, Nagoya, Aichi, 4648601, Japan. liuyuzhang95@gmail.com.

International journal of computer assisted radiology and surgery

|July 3, 2025

概括

这项研究增强了YOLO模型用于腹腔镜工具检测,提高了在最少侵入性手术中识别二叉目标 (BT) 的准确性. 新的数据增强和模型修改显著减少了错误检测,并提高了整体性能.

科学领域:

计算机视觉计算机视觉
医疗机器人医疗机器人
手术技术手术技术

背景情况:

精确的腹腔镜工具检测对于最小侵入性手术 (MIS) 的帮助至关重要.
现有的YOLO模型难以检测二叉目标 (BT),导致错误检测 (MDBT).
挑战包括错误识别BT提示或忽视小目标.

研究的目的:

增强YOLO模型,以改善腹腔镜外科手术中二叉目标 (BT) 的检测.
解决手术工具识别中错误检测分叉目标 (MDBT) 的问题.
通过精确的BT检测,改进工具姿势估计和外科导航.

主要方法:

提出了一种数据增强策略,即随机目标掩盖 (RTM),以防止BT提示错误分类.
开发了Mixup Plus (MUP) 来平衡不同类别的实例计数,具有不同的BT比例.
集成了空间到深度卷积 (SPD-Conv) 块,以改进检测小BT尖端.

主要成果:

经过修改的YOLOv8模型在EndoVis数据集上展示了改进的mAP50和mAP50:95指标.
针对BT测试数据集的具体改进包括分别在mAP50和mAP50:95上增加了~0.2和~0.1.
对于低级别的Clip Applier类别,随着拟议的修改,AP50从0.0251增加到0.457.

结论:

该研究成功地提高了使用YOLO的腹腔镜工具检测中的二叉目标 (BT) 检测精度.
随着RTM和MUP数据增强,以及SPD-Conv集成,显著提高了模型性能.
废弃研究证实了每项提议改进的有效性,验证了数据增强方法的优势.

关键词:

复杂目标识别复杂的目标识别数据增强数据增强腹腔镜检测工具检测仪器在YOLO修改中使用YOLO修改.

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