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MCPD-YOLOv3: A Novel Lightweight Detection Model for Surgical Instruments in Laparoscopic Images.

Yuqin Li^1,2, Chuqi Li¹, Ke Zhang^1,2

¹College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|August 23, 2025

Summary

This study introduces MCPD-YOLOv3 for faster and more accurate laparoscopic surgical instrument detection. The model achieves high performance with a lightweight design, suitable for real-time computer-aided surgery.

Keywords:

MCPD‐YOLOv3 laparoscopic images lightweight model surgical instruments detection

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Published on: May 23, 2025

Area of Science:

Computer Vision
Medical Imaging
Surgical Robotics

Background:

Accurate detection of laparoscopic surgical instruments is crucial for computer-aided minimally invasive surgery.
Existing methods often struggle with suboptimal performance and slow detection speeds.

Purpose of the Study:

To develop an efficient and effective framework for laparoscopic surgical instrument detection.
To balance detection accuracy and speed in real-time surgical applications.

Main Methods:

Proposed MCPD-YOLOv3 framework featuring parallel feature map fusion.
Employed lightweight strategies for model optimization.
Utilized DIoU (Distance Intersection over Union) to enhance recall performance.

Main Results:

Achieved 99.47% mAP on the ATLAS Dione dataset and 97.65% mAP on the m2cai16-tool-locations dataset.
Operates at 49.81 FPS.
Features a compact model size (12.4M) and low FLOPs (7.44G).

Conclusions:

MCPD-YOLOv3 demonstrates superior detection performance and rapid response.
The model's efficiency makes it ideal for real-time surgical instrument detection in demanding environments.