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Surgical Instrument Recognition Based on Improved YOLOv5

Applied Sciences (Switzerland), ISSN: 2076-3417, Vol: 13, Issue: 21
2023
  • 2
    Citations
  • 0
    Usage
  • 4
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    2
  • Captures
    4
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • 1

Most Recent News

Huaqiao University Researchers Add New Findings in the Area of Applied Sciences (Surgical Instrument Recognition Based on Improved YOLOv5)

2023 NOV 27 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Medical Devices Daily -- Research findings on applied sciences are discussed in

Article Description

Recognition of surgical instruments is a key part of the post-operative check and inspection of surgical instrument packaging. However, manual inventorying is prone to counting errors. The achievement of automated surgical instrument identification holds the potential to significantly mitigate the occurrence of medical accidents and reduce labor costs. In this paper, an improved You Only Look Once version 5 (YOLOv5) algorithm is proposed for the recognition of surgical instruments. Firstly, the squeeze-and-excitation (SE) attention module is added to the backbone to improve the feature extraction. Secondly, the loss function of YOLOv5 is improved with more global parameters to increase the convergence rate of the loss curve. Finally, an efficient convolution algorithm is added to the C3 module in the head to reduce computational complexity and memory usage. The experimental results show that our algorithm outperforms the original YOLOv5 with improvements observed across various metrics: mean average precision 50–95 (mAP50-95) achieved 88.7%, which improved by 1.8%, and computational requirements reduced by 39%. This study, with a simple but effective method, is expected to be a guide for automatically detecting, classifying, and sorting surgical instruments.

Bibliographic Details

Kaile Jiang; Luxuan Yang; Huaiqian Wang; Shuwan Pan; Jie Yu; Yuanda Lin

MDPI AG

Materials Science; Physics and Astronomy; Engineering; Chemical Engineering; Computer Science

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