SH-YOLO: Enhanced Real-Time Detection of Laparoscopic Surgical Instruments in Computer-Aided Surgery Based on Star Operation and Hybrid Attention Mechanisms
Real-time surgical instrument detection is essential in computer-aided surgery systems for procedure identification, quality maintenance, and operation evaluation. To meet the real-time detection requirements of laparoscopic surgical instruments, a dataset for laparoscopic surgery is established, an...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11098899/ |
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| author | Yiping Shao Zhilong Xu Qicong Zhu |
| author_facet | Yiping Shao Zhilong Xu Qicong Zhu |
| author_sort | Yiping Shao |
| collection | DOAJ |
| description | Real-time surgical instrument detection is essential in computer-aided surgery systems for procedure identification, quality maintenance, and operation evaluation. To meet the real-time detection requirements of laparoscopic surgical instruments, a dataset for laparoscopic surgery is established, and an enhanced YOLOv5 algorithm named SH-YOLO is proposed. SH-YOLO incorporates star operation, denoted as element-wise multiplication, for efficient feature extraction and a hybrid attention mechanism (HAM) for adaptive feature fusion, facilitating real-time detection of laparoscopic surgical instruments. Specifically designed for computer-assisted surgical environments, SH-YOLO addresses critical challenges including non-central object localization, subtle feature discrimination, and motion blur resilience. Experimental results show that, compared to YOLOv5, SH-YOLO significantly enhances detection speed and accuracy. Specifically, SH-YOLO achieves a mean Average Precision (mAP) of 91.10%, an F1 Score of 0.941, and a detection speed of 97.4 FPS, outperforming YOLOv5 by 8.77%, 0.03, and 35.1 FPS, respectively. These results validate SH-YOLO’s capability to meet the high accuracy and real-time requirements of instrument detection in computer-assisted laparoscopic procedures, which provides reliable technical support for intelligent surgical navigation and robotic control systems. |
| format | Article |
| id | doaj-art-6af908f80d2b445ab7def85dfd3aa802 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6af908f80d2b445ab7def85dfd3aa8022025-08-20T02:57:51ZengIEEEIEEE Access2169-35362025-01-011313517913519510.1109/ACCESS.2025.359363511098899SH-YOLO: Enhanced Real-Time Detection of Laparoscopic Surgical Instruments in Computer-Aided Surgery Based on Star Operation and Hybrid Attention MechanismsYiping Shao0https://orcid.org/0000-0002-8721-9755Zhilong Xu1https://orcid.org/0009-0004-1530-7424Qicong Zhu2https://orcid.org/0009-0009-6261-2671College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, ChinaDepartment of Gastrointestinal and Pancreatic Surgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, General Surgery, Cancer Center, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaReal-time surgical instrument detection is essential in computer-aided surgery systems for procedure identification, quality maintenance, and operation evaluation. To meet the real-time detection requirements of laparoscopic surgical instruments, a dataset for laparoscopic surgery is established, and an enhanced YOLOv5 algorithm named SH-YOLO is proposed. SH-YOLO incorporates star operation, denoted as element-wise multiplication, for efficient feature extraction and a hybrid attention mechanism (HAM) for adaptive feature fusion, facilitating real-time detection of laparoscopic surgical instruments. Specifically designed for computer-assisted surgical environments, SH-YOLO addresses critical challenges including non-central object localization, subtle feature discrimination, and motion blur resilience. Experimental results show that, compared to YOLOv5, SH-YOLO significantly enhances detection speed and accuracy. Specifically, SH-YOLO achieves a mean Average Precision (mAP) of 91.10%, an F1 Score of 0.941, and a detection speed of 97.4 FPS, outperforming YOLOv5 by 8.77%, 0.03, and 35.1 FPS, respectively. These results validate SH-YOLO’s capability to meet the high accuracy and real-time requirements of instrument detection in computer-assisted laparoscopic procedures, which provides reliable technical support for intelligent surgical navigation and robotic control systems.https://ieeexplore.ieee.org/document/11098899/Computer-aided surgeryreal-time instrument detectionYOLOfeature extractionattention mechanisms |
| spellingShingle | Yiping Shao Zhilong Xu Qicong Zhu SH-YOLO: Enhanced Real-Time Detection of Laparoscopic Surgical Instruments in Computer-Aided Surgery Based on Star Operation and Hybrid Attention Mechanisms IEEE Access Computer-aided surgery real-time instrument detection YOLO feature extraction attention mechanisms |
| title | SH-YOLO: Enhanced Real-Time Detection of Laparoscopic Surgical Instruments in Computer-Aided Surgery Based on Star Operation and Hybrid Attention Mechanisms |
| title_full | SH-YOLO: Enhanced Real-Time Detection of Laparoscopic Surgical Instruments in Computer-Aided Surgery Based on Star Operation and Hybrid Attention Mechanisms |
| title_fullStr | SH-YOLO: Enhanced Real-Time Detection of Laparoscopic Surgical Instruments in Computer-Aided Surgery Based on Star Operation and Hybrid Attention Mechanisms |
| title_full_unstemmed | SH-YOLO: Enhanced Real-Time Detection of Laparoscopic Surgical Instruments in Computer-Aided Surgery Based on Star Operation and Hybrid Attention Mechanisms |
| title_short | SH-YOLO: Enhanced Real-Time Detection of Laparoscopic Surgical Instruments in Computer-Aided Surgery Based on Star Operation and Hybrid Attention Mechanisms |
| title_sort | sh yolo enhanced real time detection of laparoscopic surgical instruments in computer aided surgery based on star operation and hybrid attention mechanisms |
| topic | Computer-aided surgery real-time instrument detection YOLO feature extraction attention mechanisms |
| url | https://ieeexplore.ieee.org/document/11098899/ |
| work_keys_str_mv | AT yipingshao shyoloenhancedrealtimedetectionoflaparoscopicsurgicalinstrumentsincomputeraidedsurgerybasedonstaroperationandhybridattentionmechanisms AT zhilongxu shyoloenhancedrealtimedetectionoflaparoscopicsurgicalinstrumentsincomputeraidedsurgerybasedonstaroperationandhybridattentionmechanisms AT qicongzhu shyoloenhancedrealtimedetectionoflaparoscopicsurgicalinstrumentsincomputeraidedsurgerybasedonstaroperationandhybridattentionmechanisms |