基于深度相机的钢筋骨架尺寸智能检测方法

doi:10.13474/j.cnki.11-2246.2024.1019.

测绘通报 ›› 2024, Vol. 0 ›› Issue (10): 114-119.doi: 10.13474/j.cnki.11-2246.2024.1019.

• 学术研究 • 上一篇

基于深度相机的钢筋骨架尺寸智能检测方法

赵训刚^1,2, 周强^1,2, 黄晓航^1,2, 钟继卫^1,2, 王波^2,3

1. 桥梁智能与绿色建造全国重点实验室, 湖北武汉 430034;
2. 中铁大桥局集团有限公司, 湖北武汉 430050;
3. 中铁大桥科学研究院有限公司, 湖北武汉 430034

收稿日期:2024-08-19 发布日期:2024-11-02
通讯作者: 周强,E-mail:315653752@qq.com
作者简介:赵训刚(1986—),男,硕士,高级工程师,从事桥梁智能建造、机器视觉方面研究。E-mail:506516369@qq.com
基金资助:
中国中铁股份有限公司科技研究开发计划(2022-专项-03;2023-重大-02)

Intelligent detection method for steel reinforcement skeleton size based on depth camera

ZHAO Xungang^1,2, ZHOU Qiang^1,2, HUANG Xiaohang^1,2, ZHONG Jiwei^1,2, WANG Bo^2,3

1. National Key Laboratory of Bridge Intelligent and Green Construction, Wuhan 430034, China;
2. China Railway Major Bridge Engineering Group Co., Ltd., Wuhan 430050, China;
3. China Railway Bridge Science Research Institute Co., Ltd., Wuhan 430034, China

Received:2024-08-19 Published:2024-11-02

摘要/Abstract

摘要： 钢筋骨架的质量检查在预制钢筋混凝土构件的生产过程中发挥了重要的作用,因为其质量直接影响整个构件的性能和可靠性。目前,对于钢筋骨架的质量检查,主要依赖于传统的人工手段,比如使用钢尺测量和人工计数来评估钢筋的数量和间距等关键指标。然而,这种方法明显存在低效率、高成本、易出错等局限性。为了解决以上问题,本文针对预制钢筋混凝土构件中的多层钢筋骨架,引入深度相机传感器和先进技术,设计了一种新颖的钢筋骨架质量检查方法。首先通过深度相机采集图像,根据深度阈值筛选算法将双层钢筋转化成单层图像;然后利用改进的YOLOv5算法检测出交叉点坐标;最后根据坐标转换,求解绑扎间距。试验结果表明,该方法通过智能化算法提高了检查的准确性,减少了时间消耗,降低了对人工的依赖。

关键词: 钢筋绑扎间距测量, 深度相机, YOLOv5算法, 机器视觉, 视觉测量

Abstract: The quality inspection of the reinforced skeleton plays an important role in the production process of prefabricated reinforced concrete components, because its quality directly affects the performance and reliability of the whole component. At present, the quality inspection of rebar skeletons mainly relies on traditional manual means, such as the use of steel ruler measurement and manual counting to evaluate key indicators such as the number and spacing of rebar spacing. However, this approach has significant limitations such as inefficiency,high cost,and error-proneness. In order to solve the above problems,this paper introduces depth camera sensor and advanced technology for the multi-layer reinforcement skeleton in the prefabricated reinforced concrete components,and designs a novel quality inspection method for the reinforcement skeleton,that is,the image is collected by the depth camera,the double-layer reinforcement is converted into a single-layer image according to the depth threshold screening algorithm,and then the intersection coordinates are detected by the improved YOLOv5 algorithm,and finally the binding spacing is solved according to the coordinate conversion. Experimental results show that the proposed method improves the accuracy of inspection,reduces time consumption,and reduces the dependence on manual labor through intelligent algorithms.

Key words: measurement of steel bar binding spacing, depth camera, YOLOv5 algorithm, computer vision, visual measurements

中图分类号:

P258

赵训刚, 周强, 黄晓航, 钟继卫, 王波. 基于深度相机的钢筋骨架尺寸智能检测方法[J]. 测绘通报, 2024, 0(10): 114-119.

ZHAO Xungang, ZHOU Qiang, HUANG Xiaohang, ZHONG Jiwei, WANG Bo. Intelligent detection method for steel reinforcement skeleton size based on depth camera[J]. Bulletin of Surveying and Mapping, 2024, 0(10): 114-119.

参考文献

[1] WANG Q,CHENG J C P,SOHN H. Automated estimation of reinforced precast concrete rebar positions using colored laser scan data[J].Computer-Aided Civil and Infrastructure Engineering,2017,32(9):787-802.
[2] KIM M K,THEDJA J P P,WANG Q.Automated dimensional quality assessment for formwork and rebar of reinforced concrete components using 3D point cloud data[J].Automation in Construction,2020,112:103077.
[3] Derpanis K G.Overview of the RANSAC algorithm[J].Image Rochester NY,2010,4(1):2-3.
[4] 刘世龙,马智亮.基于结构光相机的钢筋骨架整体点云获取算法[J].图学学报,2022,43(4):633-640.
[5] 刘世龙.基于结构光相机和BIM的PC构件钢筋骨架质量检查方法[D].北京:清华大学,2021.
[6] VOLK R,STENGEL J,SCHULTMANN F.Building information modeling (BIM) for existing buildings—literature review and future needs[J].Automation in Construction,2014,38:109-127.
[7] ZHIHUA XU,LINGLING QU.A skeletal camera network for close-range images with a data driven ap-proach in analyzing stereo configuration[J].Journal of Geodesy and Geoinformation Science,2022,5(4):23-37.
[8] 闫天冉,马晓静,饶颖露,等.基于改进Mask R-CNN的建筑钢筋尺寸检测算法[J].计算机工程,2021,47(9):274-281.
[9] 石凌晨.基于计算机视觉的钢筋排布检测[D].重庆:重庆大学,2022.
[10] HE K M,GKIOXARI G,DOLLÁR P,et al.Mask R-CNN[C]//Proceedings of 2017 IEEE International Conference on Computer Vision.Venice:IEEE,2017:2980-2988.
[11] 李子轩,赵志刚,张泽宇,等.基于FNB-YOLOv5的钢筋网绑扎点目标检测[J/OL].上海交通大学学报,1-24[2024-06-14].https://doi.org/10.16183/j.cnki.jsjtu.2024.121.
[12] 成彬,邓磊.融合深度学习与特征投影曲线的钢筋绑扎节点检测方法[J/OL].计算机辅助设计与图形学学报,1-15[2024-06-14].http://kns.cnki.net/kcms/detail/11.2925.TP.20240204.1104.012.html.
[13] 凤若成.基于轻量卷积神经网络的钢筋交点识别算法[J].价值工程,2024,43(13):107-109.
[14] AN M S,KANG D S.The distance measurement based on corner detection for rebar spacing in engineering images[J].The Journal of Supercomputing,2022,78(10):12380-12393.
[15] QIAO J,CUI J.Image-driven recognition of crossing points in rebar lashing for PC component algorithm[EB/OL].[2024-07-20].https://www.researchsquare.com/article/rs-1482262/v1.
[16] KIM J H,KIM N,PARK Y W,et al.Object detection and classification based on YOLO-V5 with improved maritime dataset[J].Journal of Marine Science and Engineering,2022,10(3):377.
[17] HOWARD A G,ZHU M L,CHEN B,et al.MobileNets:efficient convolutional neural networks for mobile vision applications[EB/OL].[2024-07-21].https://arxiv.org/abs/1704.04861v1.
[18] KRISHNA K,NARASIMHA MURTY M.Genetic K-means algorithm[J].IEEE Transactions on Systems,Man,and Cybernetics Part B,Cybernetics,1999,29(3):433-439.
[19] SANDLER M,HOWARD A,ZHU M L,et al.MobileNetV2:inverted residuals and linear bottlenecks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:4510-4520.

基于深度相机的钢筋骨架尺寸智能检测方法

Intelligent detection method for steel reinforcement skeleton size based on depth camera

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 5

编辑推荐

Metrics

本文评价

[1]	何雨薇, 诸葛盛, 徐祥鹏, 钟立军, 杨夏, 张小虎. 桥梁自然特征点的快速精确跟踪算法[J]. 测绘通报, 2023, 0(5): 101-106,163.
[2]	时梦杰, 陶庭叶, 高飞, 李江洋, 陈皓. 雾天环境下机器视觉的边坡监测方法[J]. 测绘通报, 2022, 0(6): 88-92.
[3]	范军林, 肖斌, 涂梨平, 胡全一, 师现杰, 危双丰. RGB-D SLAM在室内高精度三维测图中的应用[J]. 测绘通报, 2021, 0(8): 119-122.
[4]	李军, 朱国琦, 樊晓东, 杨维, 黄震. 地铁隧道结构机器视觉检测系统及应用分析[J]. 测绘通报, 2020, 0(9): 27-32,37.
[5]	严成, 何宁, 庞维庆, 邓德迎. 基于机器视觉的水域远程监测应用系统方案设计[J]. 测绘通报, 2019, 0(8): 149-153.