Machine Vision-Based Surgical Lighting System:Design and Implementation

TL;DR

This paper presents a machine vision-based surgical lighting system that automatically adjusts illumination using YOLOv11 detection of a blue marker, reducing surgeon fatigue and improving lighting consistency during procedures.

Contribution

It introduces a novel automated surgical lighting system utilizing YOLOv11 for real-time marker detection and servo-controlled light positioning, enhancing precision and ergonomics.

Findings

YOLOv11 achieves 96.7% mAP@50 on surgical scene images.

The system effectively automates lighting adjustment, reducing surgeon fatigue.

Improves consistency and safety of surgical illumination.

Abstract

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.