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UK-YOLOv10: Deep Learning-Based Detection of Surgical Instruments.
Li Zhang1, Guanqun Guo1, Wenjie Wang1
1School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China.
Summary
UK-YOLOv10 enhances surgical instrument detection for robot-assisted surgery using novel attention and KAN convolution modules. This advanced framework achieves superior accuracy and real-time performance, improving clinical applicability.
Area of Science:
- Computer Vision
- Robotics
- Medical Imaging
Background:
- Accurate surgical instrument detection is critical for robot-assisted surgery.
- Current methods struggle with accuracy and real-time performance, hindering clinical use.
Purpose of the Study:
- To introduce UK-YOLOv10, a novel framework for enhanced surgical instrument detection.
- To improve accuracy and real-time capabilities in robot-assisted surgical procedures.
Main Methods:
- Integration of the uni-fusion attention module (UFAM) for superior multi-scale feature representation.
- Implementation of the C2fKAN module with KAN convolution to boost classification accuracy and training speed.
Main Results:
- UK-YOLOv10 achieved 96.7% detection accuracy on the M2CAI16-Tool-Locations dataset.
- Outperformed YOLOv10 with higher mAP@0.5 (96.4%) and mAP@0.5:0.95 (0.605).
- Demonstrated strong generalization on the COCO2017 dataset with an mAP@0.5:0.95 of 0.386.
Conclusions:
- UK-YOLOv10 significantly advances surgical instrument detection.
- The framework shows substantial promise for enhancing robot-assisted surgery applications.
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