Surgical Instrument Detection Algorithm Based on Improved YOLOv7x
The counting of surgical instruments is an important task to ensure surgical safety and patient health. However, due to the uncertainty of manual operations, there is a risk of missing or miscounting instruments. Applying computer vision technology to the instrument counting process can not only imp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/11/5037 |
| _version_ | 1797596743416676352 |
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| author | Boping Ran Bo Huang Shunpan Liang Yulei Hou |
| author_facet | Boping Ran Bo Huang Shunpan Liang Yulei Hou |
| author_sort | Boping Ran |
| collection | DOAJ |
| description | The counting of surgical instruments is an important task to ensure surgical safety and patient health. However, due to the uncertainty of manual operations, there is a risk of missing or miscounting instruments. Applying computer vision technology to the instrument counting process can not only improve efficiency, but also reduce medical disputes and promote the development of medical informatization. However, during the counting process, surgical instruments may be densely arranged or obstruct each other, and they may be affected by different lighting environments, all of which can affect the accuracy of instrument recognition. In addition, similar instruments may have only minor differences in appearance and shape, which increases the difficulty of identification. To address these issues, this paper improves the YOLOv7x object detection algorithm and applies it to the surgical instrument detection task. First, the RepLK Block module is introduced into the YOLOv7x backbone network, which can increase the effective receptive field and guide the network to learn more shape features. Second, the ODConv structure is introduced into the neck module of the network, which can significantly enhance the feature extraction ability of the basic convolution operation of the CNN and capture more rich contextual information. At the same time, we created the OSI26 data set, which contains 452 images and 26 surgical instruments, for model training and evaluation. The experimental results show that our improved algorithm exhibits higher accuracy and robustness in surgical instrument detection tasks, with F1, AP, AP50, and AP75 reaching 94.7%, 91.5%, 99.1%, and 98.2%, respectively, which are 4.6%, 3.1%, 3.6%, and 3.9% higher than the baseline. Compared to other mainstream object detection algorithms, our method has significant advantages. These results demonstrate that our method can more accurately identify surgical instruments, thereby improving surgical safety and patient health. |
| first_indexed | 2024-03-11T02:57:19Z |
| format | Article |
| id | doaj.art-1587185a067e488f8213e645c4772b3e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:57:19Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1587185a067e488f8213e645c4772b3e2023-11-18T08:31:39ZengMDPI AGSensors1424-82202023-05-012311503710.3390/s23115037Surgical Instrument Detection Algorithm Based on Improved YOLOv7xBoping Ran0Bo Huang1Shunpan Liang2Yulei Hou3School of Information Science and Engineering, Yanshan University, Qinhuangdao 066000, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066000, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066000, ChinaSchool of Mechanical Engineering, Yanshan University, Qinhuangdao 066000, ChinaThe counting of surgical instruments is an important task to ensure surgical safety and patient health. However, due to the uncertainty of manual operations, there is a risk of missing or miscounting instruments. Applying computer vision technology to the instrument counting process can not only improve efficiency, but also reduce medical disputes and promote the development of medical informatization. However, during the counting process, surgical instruments may be densely arranged or obstruct each other, and they may be affected by different lighting environments, all of which can affect the accuracy of instrument recognition. In addition, similar instruments may have only minor differences in appearance and shape, which increases the difficulty of identification. To address these issues, this paper improves the YOLOv7x object detection algorithm and applies it to the surgical instrument detection task. First, the RepLK Block module is introduced into the YOLOv7x backbone network, which can increase the effective receptive field and guide the network to learn more shape features. Second, the ODConv structure is introduced into the neck module of the network, which can significantly enhance the feature extraction ability of the basic convolution operation of the CNN and capture more rich contextual information. At the same time, we created the OSI26 data set, which contains 452 images and 26 surgical instruments, for model training and evaluation. The experimental results show that our improved algorithm exhibits higher accuracy and robustness in surgical instrument detection tasks, with F1, AP, AP50, and AP75 reaching 94.7%, 91.5%, 99.1%, and 98.2%, respectively, which are 4.6%, 3.1%, 3.6%, and 3.9% higher than the baseline. Compared to other mainstream object detection algorithms, our method has significant advantages. These results demonstrate that our method can more accurately identify surgical instruments, thereby improving surgical safety and patient health.https://www.mdpi.com/1424-8220/23/11/5037deep learningYOLOV7xsurgical instrument detectioncomputer vision |
| spellingShingle | Boping Ran Bo Huang Shunpan Liang Yulei Hou Surgical Instrument Detection Algorithm Based on Improved YOLOv7x Sensors deep learning YOLOV7x surgical instrument detection computer vision |
| title | Surgical Instrument Detection Algorithm Based on Improved YOLOv7x |
| title_full | Surgical Instrument Detection Algorithm Based on Improved YOLOv7x |
| title_fullStr | Surgical Instrument Detection Algorithm Based on Improved YOLOv7x |
| title_full_unstemmed | Surgical Instrument Detection Algorithm Based on Improved YOLOv7x |
| title_short | Surgical Instrument Detection Algorithm Based on Improved YOLOv7x |
| title_sort | surgical instrument detection algorithm based on improved yolov7x |
| topic | deep learning YOLOV7x surgical instrument detection computer vision |
| url | https://www.mdpi.com/1424-8220/23/11/5037 |
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