Citation:
Ziqiang Li, Lei Ren, Li Liu, Zuohua Miao. Intelligent Detection of Unsafe State on Construction Site Based on Yolov5 Algorithm. Journal of Information Technologyin Civil Engineering and Architecture,
2023, 15(3): 20-26.
doi: 10.16670/j.cnki.cn11-5823/tu.2023.03.04
2023, 15(3): 20-26. doi: 10.16670/j.cnki.cn11-5823/tu.2023.03.04
Intelligent Detection of Unsafe State on Construction Site Based on Yolov5 Algorithm
| 1. | School of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China |
| 2. | School of Resources and Environmental Engineering, Wuhan University of Science and Technology, Wuhan 430081, China |
| 3. | Key Laboratory of Hubei Province for Efficient Utilization of Metallurgical Mineral Resources and Block Building, Wuhan 430081, China |
In order to improve the safety supervision of workers on the construction site, yolov5 target recognition algorithm combined with UAV tilt photography three-dimensional modeling technology are applied to create the intelligent detection model of unsafe state on the construction site to identify and position human, machinery and other targets. Through experimental comparison and analysis, the paper determines the optimal target recognition algorithm and constructs the multi-target recognition model. The results are in line with the theoretical conjecture, and the overall average recognition accuracy reaches 91.6%. After recognizing the target, its relative position is further determined with the spatial position information provided by the tilt photography three-dimensional model, which manages to ensure the safety state of workers. The accuracy of this visual positioning is determined by the three-dimensional model. It is verified that the distance error of the three-dimensional model constructed by UAV tilt photography is about 1.5% and the range length is greater than 35m, hence the distance error will be less than 1%, which notes the high accuracy for the distance of the object recognized by the target recognition model.
| [1] |
姜玥麒, 袁永博, 张明媛. 基于AHP模糊综合评价的建筑工人不安全行为传播性研究[J]. 工程管理学报, 2021, 35(02): 131-136. |
| [2] |
闫高峰. 基于VR技术的建筑工程施工安全管理体系分析[J]. 山西建筑, 2021, 47(09): 189-191.doi: 10.13719/j.cnki.1009-6825.2021.09.069 |
| [3] |
刘文平. 基于BIM与定位技术的施工事故预警机制研究[D]. 清华大学, 2015. |
| [4] |
郭红领, 于言滔, 刘文平, 等. BIM和RFID在施工安全管理中的集成应用研究[J]. 工程管理学报, 2014, 28(04): 87-92. |
| [5] |
赵一秾, 李若熙, 曹语含, 等. 双流卷积网络工人异常行为识别算法研究[J]. 辽宁科技大学学报, 2019, 42(04): 301- 308.doi: 10.13988/j.ustl.2019.04.013 |
| [6] | |
| [7] |
Dong S, He Q, Li H, et al. Automated PPE Misuse Identification and Assessment for Safety Performance Enhancement[C]. 2015, 13(12): 13-16. |
| [8] |
Khairullah M, Habibur Rahman M, M. H B S. BlueAd: A Location based Service using Bluetooth[J]. International Journal of Computer Applications. 2012, 43(15): 19-22.doi: 10.5120/6179-8608 |
| [9] |
Kelm A, Laußat L, Meins-Becker A, et al. Mobile passive Radio Frequency Identification (RFID) portal for automated and rapid control of Personal Protective Equipment (PPE) on sites[J]. Automation in Construction. 2013, 36(36): 38- 52. |
| [10] |
胡劲松, 赵国强, 陈立生, 等. 一种基于RFID安全帽的实时监控系统及其应用方法[P]. 上海: CN103116926A, 2013-05-22. |
| [11] |
Barro-Torres S, Fernández-Caramés T M, Pérez-Iglesias H J, et al. Real-timepersonal protective equipment monitoring system[J]. Computer Communications. 2012, 36(1): 42-50. |
| [12] |
张明媛, 曹志颖, 赵雪峰, 等. 基于深度学习的建筑工人安全帽佩戴识别研究[J]. 安全与环境学报, 2019, 19(02): 535-541. |
| [13] |
高寒, 骆汉宾, 方伟立. 基于机器视觉的施工危险区域侵入行为识别方法[J]. 土木工程与管理学报, 2019, 36(01): 123-128. |
| [14] |
Han S U, Pena-Mora F. Vision-based detection of a unsafe actions of a construction worker: Case study ladder climbing[J]. Journal of Computing in Civil EngIneering, 2013, 27(6): 635-644. |
| [15] |
Kolar Z, Chen H, Luo X. Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images[J]. Automation in Construction, 2018, 89: 58- 70. |
| [16] |
Fang Q, Li H, Luo X, et al. Detecting non-hardhatuse by a deep learning method from farfield surveillance videos[J]. Automation in Construction, 2018, 85(03): 1-9. |
| [17] |
王伟, 吕山可, 张雨果, 等. 基于BIM与机器视觉技术结合的建筑施工危险区域入侵预警研究[J]. 安全与环境工程, 2020, 27(02): 196-203. |
| [18] |
王毅恒, 许德章. 基于YOLOv3算法的农场环境下奶牛目标识别[J]. 广东石油化工学院学报, 2019, 29(04): 31-35. |
| [19] |
刘晓慧, 叶西宁. 肤色检测和Hu矩在安全帽识别中的应用[J]. 华东理工大学学报(自然科学版). 2014, 40(3): 19-25. |
| [20] |
Rubaiyat A H M, Toma T T, Kalantari-Khandani M, et al. Automatic Detection of Helmet Uses for Construction Safety[C]. 2017, 14(02): 29-32. |
| [21] |
任磊, 苗作华, 李自强, 等. 基于YOLOv3算法的危险区域工人识别[J]. 土木建筑工程信息技术. (网络首发) |
| [22] |
苗作华, 任磊, 王梦婷, 等. 露天矿山无人机倾斜摄影测量数据的准确性探究[J]. 矿业研究与开发, 2022, 42(02): 164-168. |
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