RESEARCH ON SAFETY MONITORING METHOD FORWIND POWER PROJECT CONSTRUCTION SITE AREA BASED ONBINOCULAR STEREO VISION DETECTION TECHNOLOGY

Wang Yajun; Zhang Xu; Zheng Wenjin; Fang Shibiao

doi:10.19912/j.0254-0096.tynxb.2025-0325

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 90-97. DOI: 10.19912/j.0254-0096.tynxb.2025-0325

RESEARCH ON SAFETY MONITORING METHOD FORWIND POWER PROJECT CONSTRUCTION SITE AREA BASED ONBINOCULAR STEREO VISION DETECTION TECHNOLOGY

Wang Yajun¹, Zhang Xu¹, Zheng Wenjin¹, Fang Shibiao²

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Abstract

To address the frequent collision accidents among pedestrians, vehicles, and heavy equipment at renewable energy construction sites (wind farms/solar power stations）, this study develops a high-precision real-time traffic monitoring system by integrating machine vision and deep learning technologies within a multi-modal monitoring framework to enhance dynamic risk perception. An improved YOLO v8 model is employed for object detection, combined with binocular stereo vision and Kalman filtering algorithms for three-dimensional positioning and trajectory prediction. A prototype system is deployed at the Lingbi County wind power project, with its performance validated through real-time video streams. Experimental results demonstrate that the system achieves pedestrian detection accuracy of 96.2% and vehicle recognition accuracy of 98.5% under construction scenarios, while simultaneously tracking transport vehicle dynamic parameters (distance error ±3.0 m, speed error ±1.2 km/h）, and improves response speed by 40% compared to conventional monitoring approaches. This study provides a universal solution for intelligent safety management at renewable energy construction sites, facilitating the deep integration of smart construction sites and intelligent transportation systems.

Key words

machine vision / renewable energy / blades / dynamic risk perception / three-dimensional spatial positioning

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Wang Yajun, Zhang Xu, Zheng Wenjin, Fang Shibiao. RESEARCH ON SAFETY MONITORING METHOD FORWIND POWER PROJECT CONSTRUCTION SITE AREA BASED ONBINOCULAR STEREO VISION DETECTION TECHNOLOGY[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 90-97 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0325

References

[1] 石腾, 许波峰, 陈鹏, 等. 基于机器视觉的风电机组叶片多类型损伤检测方法研究[J]. 太阳能学报, 2024, 45(6): 487-494.
Shi T, Xu B F, Chen P, et al.Study on multi-type damage detection method for wind turbine blades based on machine vision technology[J]. Acta Energiae Solaris Sinica, 2024, 45(6): 487-494.
[2] 邬伟骏, 吴江波, 周强, 等. 基于贝叶斯推理的风电机组风轮偏航协同智能控制方法[J]. 可再生能源, 2024, 42(9): 1205-1210.
Wu W J, Wu J B, Zhou Q, et al.Collaborative intelligent control method for wind turbine rotor yaw based on Bayesian inference[J]. Renewable Energy Resources, 2024, 42(9): 1205-1210.
[3] 贾晓红, 石岚, 郝玉珠. 基于风电场片区风速的人工智能插补方法对比[J]. 太阳能学报, 2025, 46(1): 168-175.
Jia X H, Shi L, Hao Y Z.Comparison of artificial intelligence interpolation methods based on wind speed of wind farm divided into wind turbine groups[J]. Acta Energiae Solaris Sinica, 2025, 46(1): 168-175.
[4] Musial W, Spitsen P, Duffy P, et al.Offshore wind market report: 2021 edition[R]. U.S. Department of Energy, 2021.
[5] International Renewable Energy Agency}. Renewable capacity statistics 2023[R]. Abu Dhabi: International Renewable Energy Agency, 2023.
[6] 戴丽萍, 张泽能, 丛龙福, 等. 基于神经网络的风力机叶片三维失速模型研究[J]. 太阳能学报, 2025, 46(1): 53-59.
Dai L P, Zhang Z N, Cong L F, et al.Study on three-dimensional stall model of wind turbine blades based on neural networks[J]. Acta Energiae Solaris Sinica, 2025, 46(1): 53-59.
[7] 全球能源互联网发展合作组织. 全球清洁能源开发与投资研究[M]. 北京: 中国电力出版社, 2020.
GEIDCO. Research on global clean energy development and investment[M]. Beijing: China Electric Power Press, 2020.
[8] 邹龙洲, 王文韫, 郭迎福, 等. 基于改进YOLOv4的风力机叶片损伤检测方法[J]. 太阳能学报, 2024, 45(7): 718-723.Zou L Z, Wang W Y, Guo Y F, et al. Wind turbine blade damage detection method based on improved YOLOv4[J]. Acta Energiae Solaris Sinica, 2024, 45(7): 718-723.
[9] 徐一鸣, 张娟, 刘成成, 等. 迁移学习模式下基于GoogLeNet网络的风电机组视觉检测[J]. 计算机科学, 2019, 46(5): 260-265.
Xu Y M, Zhang J, Liu C C, et al.Wind turbine visual inspection based on GoogLeNet network in transfer learning mode[J]. Computer Science, 2019, 46(5): 260-265.
[10] 李奕炜, 骆立实, 赵波, 等. 基于YOLO v8算法的变电站视频监控多目标智能跟踪方法[J]. 电信科学, 2025, 41(3): 179-189.
Li Y W, Luo L S, Zhao B, et al.Multi-target intelligent tracking method for substation video surveillance based on YOLO v8 algorithm[J]. Telecommunications Science, 2025, 41(3): 179-189.
[11] 王文亮, 解锡伟, 徐玉龙, 等. 基于物联网的风力发电机塔筒倾斜监测系统设计[J]. 工业仪表与自动化装置, 2024(5): 40-43, 64.
Wang W L, Xie X W, Xu Y L, et al.Design of a wind turbine tower tilt monitoring system based on the Internet of Things[J]. Industrial Instrumentation & Automation, 2024(5): 40-43, 64.
[12] Liu X T, Hu Y T, Ji H T, et al.A deep learning method for ship detection and traffic monitoring in an offshore wind farm area[J]. Journal of Marine Science and Engineering, 2023, 11(7): 1259.
[13] 王文举, 唐邦, 顾泽骅, 等. 深度学习的多视角三维重建技术综述[J]. 计算机工程与应用, 2025, 61(6): 22-35.
Wang W J, Tang B, Gu Z H, et al.Overview of multi-view 3D reconstruction techniques in deep learning[J]. Computer Engineering and Applications, 2025, 61(6): 22-35.