GS-LinYOLOv10: A drone-based model for real-time construction site safety monitoring
Real-time safety monitoring on construction sites is essential for ensuring worker safety, but traditional detection methods face challenges in dynamic environments with moving objects, occlusions, and complex conditions. To address these limitations, we propose GS-LinYOLOv10, an improved model base...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825000304 |
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| _version_ | 1823856874380853248 |
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| author | Yang Song ZhenLin Chen Hua Yang Jifei Liao |
| author_facet | Yang Song ZhenLin Chen Hua Yang Jifei Liao |
| author_sort | Yang Song |
| collection | DOAJ |
| description | Real-time safety monitoring on construction sites is essential for ensuring worker safety, but traditional detection methods face challenges in dynamic environments with moving objects, occlusions, and complex conditions. To address these limitations, we propose GS-LinYOLOv10, an improved model based on YOLOv10, specifically designed for drone-based safety monitoring. The GSConv module introduces a lightweight feature extraction mechanism, reducing computational complexity without compromising detection accuracy. The Linformer-based attention mechanism efficiently captures global context, addressing challenges in dynamic and complex environments. The model integrates IoT sensor data for real-time feedback, incorporates the GSConv module for lightweight feature extraction, and utilizes a Linformer-based attention mechanism to efficiently capture global context. These innovations reduce computational complexity while significantly improving detection accuracy. Experimental results show that GS-LinYOLOv10 achieves a precision of 91.2% and a mean average precision (mAP) of 89.4%, outperforming existing models. The integration of IoT sensors allows the drone system to dynamically adjust its monitoring focus, improving adaptability to changing environments and enhancing hazard detection. This research provides an advanced, drone-based IoT-enhanced solution for real-time construction site safety monitoring, offering a more effective and efficient approach to safety management. |
| format | Article |
| id | doaj-art-026e4146a87745f9a3a6f02316607a86 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-026e4146a87745f9a3a6f02316607a862025-02-12T05:30:42ZengElsevierAlexandria Engineering Journal1110-01682025-05-011206273GS-LinYOLOv10: A drone-based model for real-time construction site safety monitoringYang Song0ZhenLin Chen1Hua Yang2Jifei Liao3College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China; Corresponding author.School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430048, ChinaCollege of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, ChinaReal-time safety monitoring on construction sites is essential for ensuring worker safety, but traditional detection methods face challenges in dynamic environments with moving objects, occlusions, and complex conditions. To address these limitations, we propose GS-LinYOLOv10, an improved model based on YOLOv10, specifically designed for drone-based safety monitoring. The GSConv module introduces a lightweight feature extraction mechanism, reducing computational complexity without compromising detection accuracy. The Linformer-based attention mechanism efficiently captures global context, addressing challenges in dynamic and complex environments. The model integrates IoT sensor data for real-time feedback, incorporates the GSConv module for lightweight feature extraction, and utilizes a Linformer-based attention mechanism to efficiently capture global context. These innovations reduce computational complexity while significantly improving detection accuracy. Experimental results show that GS-LinYOLOv10 achieves a precision of 91.2% and a mean average precision (mAP) of 89.4%, outperforming existing models. The integration of IoT sensors allows the drone system to dynamically adjust its monitoring focus, improving adaptability to changing environments and enhancing hazard detection. This research provides an advanced, drone-based IoT-enhanced solution for real-time construction site safety monitoring, offering a more effective and efficient approach to safety management.http://www.sciencedirect.com/science/article/pii/S1110016825000304Drone-based monitoringConstruction site safetyIoT integrationGSConvReal-time safety detectionLinformer |
| spellingShingle | Yang Song ZhenLin Chen Hua Yang Jifei Liao GS-LinYOLOv10: A drone-based model for real-time construction site safety monitoring Alexandria Engineering Journal Drone-based monitoring Construction site safety IoT integration GSConv Real-time safety detection Linformer |
| title | GS-LinYOLOv10: A drone-based model for real-time construction site safety monitoring |
| title_full | GS-LinYOLOv10: A drone-based model for real-time construction site safety monitoring |
| title_fullStr | GS-LinYOLOv10: A drone-based model for real-time construction site safety monitoring |
| title_full_unstemmed | GS-LinYOLOv10: A drone-based model for real-time construction site safety monitoring |
| title_short | GS-LinYOLOv10: A drone-based model for real-time construction site safety monitoring |
| title_sort | gs linyolov10 a drone based model for real time construction site safety monitoring |
| topic | Drone-based monitoring Construction site safety IoT integration GSConv Real-time safety detection Linformer |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825000304 |
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