UK-YOLOv10: Deep Learning-Based Detection of Surgical Instruments
Related Experiment Video
Updated: Sep 17, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Li Zhang1, Guanqun Guo1, Wenjie Wang1
1School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China.
Background:
Accurate detection of surgical instruments is essential for robot-assisted surgery. Existing methods face challenges in both accuracy and real-time performance, limiting their clinical applicability.
Methods:
We propose UK-YOLOv10, a novel framework that integrates two innovations: the uni-fusion attention module (UFAM) for enhanced multi-scale feature representation, and the C2fKAN module, which employs KAN convolution to improve classification accuracy and accelerate training.
Results:
On the M2CAI16-Tool-Locations dataset, UK-YOLOv10 achieves detection accuracy of 96.7%, an mAP@0.5 of 96.4%, and an mAP@0.5:0.95 of 0.605, outperforming YOLOv10 by 3%, 2.2% and 3.6%, respectively. Generalisation on COCO2017 resulted in an mAP@0.5:0.95 of 0.386.
Conclusion:
UK-YOLOv10 significantly improves surgical instrument detection and demonstrates strong potential for robot-assisted surgeries.
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