Surgical Instrument Recognition Based on Improved YOLOv5
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Published:2023-10-26
Issue:21
Volume:13
Page:11709
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Jiang Kaile1, Pan Shuwan2ORCID, Yang Luxuan1, Yu Jie2, Lin Yuanda2, Wang Huaiqian12ORCID
Affiliation:
1. College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China 2. College of Engineering, Huaqiao University, Quanzhou 362021, China
Abstract
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.
Funder
High Level Talent Innovation and Entrepreneurship Project of Quanzhou University Industry Education Cooperation Project of Fujian Province Fundamental Research Funds for the Central Universities Collaborative Innovation Platform Project of Fuzhou-Xiamen-Quanzhou National Independent Innovation Demonstration Zone
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference24 articles.
1. Xu, Y., Tong, X., Mao, Y., Griffin, W.B., Kannan, B., and DeRose, L.A. (June, January 31). A vision-Guided Robot Manipulator for Surgical Instrument Singulation in A Cluttered Environment. Proceedings of the 2014 IEEE International Conference on Robotics and Automation, Hong Kong, China. 2. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA. 3. Tan, M., Pang, R., and Le, Q.V. (2020, January 13–19). Efficientdet: Scalable and Efficient Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA. 4. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks;Ren;Adv. Neural Inf. Process. Syst.,2015 5. Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment;Singh;Multimed. Tools Appl.,2021
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