YOLOv10同时定位手术中双手并判断左右，提升紧急手术决策效率。
YOLOv10-Based Multi-Task Framework for Hand Localization and Laterality Classification in Surgical Videos
- 基于YOLOv10设计多任务框架，联合检测手部位置与左右侧别。
- 左/右手分类准确率分别为67%和71%，实时推理性能满足术中需求。
- 适用于创伤外科视频分析，助力手-器械交互行为研究。
在创伤外科中，实时追踪手部对快速精准的术中决策至关重要。本文提出一种基于YOLOv10的多任务框架，可在复杂手术场景中同时定位双手并分类其左右侧（左或右）。模型在包含第一人称手术视频及标注手部边界框的Trauma THOMPSON Challenge 2025 Task 2数据集上训练，通过大量数据增强和多任务检测设计，提升了对运动模糊、光照变化及多样手部外观的鲁棒性。评估结果显示，左/右手分类准确率分别达67%和71%，而区分手部与背景仍具挑战；模型实现$ mAP_{[0.5:0.95]} $为0.33，并保持实时推理能力，展现出在术中部署的潜力。该工作为紧急手术中手-器械交互分析奠定了基础。
原文摘要 · Abstract (English)
Real-time hand tracking in trauma surgery is essential for supporting rapid and precise intraoperative decisions. We propose a YOLOv10-based framework that simultaneously localizes hands and classifies their laterality (left or right) in complex surgical scenes. The model is trained on the Trauma THOMPSON Challenge 2025 Task 2 dataset, consisting of first-person surgical videos with annotated hand bounding boxes. Extensive data augmentation and a multi-task detection design improve robustness against motion blur, lighting variations, and diverse hand appearances. Evaluation demonstrates accurate left-hand (67\%) and right-hand (71\%) classification, while distinguishing hands from the background remains challenging. The model achieves an $mAP_{[0.5:0.95]}$ of 0.33 and maintains real-time inference, highlighting its potential for intraoperative deployment. This work establishes a foundation for advanced hand-instrument interaction analysis in emergency surgical procedures.
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