| 宋本扬,崔博,张天伟,吴斌平,任炳昱,佟大威.碾压混凝土坝仓面碾压及摊铺状态视觉监控方法[J].水利学报,2025,56(4):445-454,465 |
| 碾压混凝土坝仓面碾压及摊铺状态视觉监控方法 |
| Visual monitoring method forthe rolling and paving status of the roller compacted concrete dam |
| 投稿时间：2024-04-03 |
| DOI：10.13243/j.cnki.slxb.20240185 |
| 中文关键词: 碾压混凝土坝 水利工程施工 视频监控 深度学习 计算机视觉 碾压施工 |
| 英文关键词: roller compacted concrete dams construction of hydraulic engineering video monitoring deep learning computer vision rolling construction |
| 基金项目:国家自然科学基金项目(U23B20148);华能澜沧江水电股份有限公司基金项目(HNKJ20-H21TB) |
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| 中文摘要: |
| 碾压混凝土坝坝面施工时间控制对浇筑质量影响巨大，而碾压混凝土坝坝面施工环境复杂，指派专人定时定点手动记录的传统方式存在人员成本大、效率低和安全管理难度高等难题。针对以上问题，本文提出一种视觉化的碾压混凝土坝仓面碾压及摊铺状态智能监控和分析方法。通过利用布置于施工现场的视频监控系统获取坝面施工的图像数据，采用QueryInst深度学习算法对坝面各区域施工状态自动化识别，并考虑图像数据的时间关系，实时统计分析现场施工阶段和各阶段施工时间。本方法应用于某碾压混凝土坝仓面施工状态的监控过程，应用效果表明：所提方法能够准确、有效地识别仓面各区域的施工状态并自动统计各施工阶段的时长，从而为施工现场管理提供出丰富的数据支持和参考依据。 |
| 英文摘要: |
| The control of construction time for roller compacted concrete (RCC) dam significantly impacts the quality of pouring. However, the environment of RCC dams is complex, and the traditional method of assigning dedicated personnel to manually record at fixed times and locations faces challenges such as high personnel costs, low efficiency, and difficulties in safety management. To address the above issues, this article proposes a visualbased intelligent monitoring and analysis method for the rolling and paving status of RCC dams. Using a video surveillance system arranged on the construction site to obtain image data of construction, the QueryInst deep learning algorithm automatically identifies the construction status of various areas on the dam surface. Considering the relationship of the image data, statistical analysis is conducted on the construction stage and construction time of each stage on site. This method has been applied to the monitoring process of the construction status of a RCC dam in Southwest China. The results show that the proposed method can accurately and effectively identify the construction status of various areas on the warehouse surface and calculate the duration of each construction stage, providing rich data support and reference basis for better management level at construction sites. |
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