用视觉识别自动调光，减少手术中医生疲劳
Machine Vision-Based Surgical Lighting System:Design and Implementation
- 用YOLOv11识别蓝色标记定位光源
- 验证集上检测准确率达96.7% mAP@50
- 适合需要稳定光照的微创手术场景
手术照明的便捷与人体工学设计对精准与安全至关重要。然而，传统系统多依赖手动调节，易导致医生疲劳、颈部不适以及因漂移和阴影造成的照明显著不均。为解决此问题，我们提出一种基于机器视觉的新型手术照明系统：利用YOLOv11目标检测算法识别置于手术部位上方的蓝色球形标记，再通过双伺服电机驱动高功率LED灯源精准对准该位置。在包含模拟手术场景的标注图像验证集上，该模型mAP@50达到96.7%。该自动化照明方案有效减轻医生体力负担，提升光照一致性，有助于改善手术效果。
原文摘要 · Abstract (English)
Effortless and ergonomically designed surgical lighting is critical for precision and safety during procedures. However, traditional systems often rely on manual adjustments, leading to surgeon fatigue, neck strain, and inconsistent illumination due to drift and shadowing. To address these challenges, we propose a novel surgical lighting system that leverages the YOLOv11 object detection algorithm to identify a blue marker placed above the target surgical site. A high-power LED light source is then directed to the identified location using two servomotors equipped with tilt-pan brackets. The YOLO model achieves 96.7% mAP@50 on the validation set consisting of annotated images simulating surgical scenes with the blue spherical marker. By automating the lighting process, this machine vision-based solution reduces physical strain on surgeons, improves consistency in illumination, and supports improved surgical outcomes.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。