基于沙漏网络的服役钢桥螺栓松动检测算法

刘燕妮; 赵伟; 陈涵深; 吕硕; 张伟

doi:10.13206/j.gjgS24022901

基于沙漏网络的服役钢桥螺栓松动检测算法

doi: 10.13206/j.gjgS24022901

刘燕妮¹,
赵伟^2, ,,
陈涵深²,
吕硕³,
张伟⁴

1. 内蒙古科技大学土木工程学院, 内蒙古包头 014000;
2. 浙江交通职业技术学院, 教育部公路水运钢结构桥梁应用协同创新中心, 杭州 311112;
3. 山东省路桥集团有限公司, 济南 250014;
4. 贵州省山区高速公路智慧运维工程研究中心, 贵阳 550000

基金项目:

浙江省道桥检测与养护技术研究重点实验室开放课题(202202Z)

贵州省交通运输厅科技计划项目(2024008)。

详细信息

作者简介:
刘燕妮,硕士,主要从事钢结构图像识别技术研究,2483836156@qq.com。

通讯作者:
赵伟,教授,主要从事钢桥教学与科研工作,zhaowei7811@126.com。

计量
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- 被引次数: 0
出版历程
- 收稿日期: 2024-02-29
- 网络出版日期: 2025-03-24

A Bolt Loosening Detection Algorithm for Steel Bridges Based on the Hourglass Network

1. College of Civil Engineering, Inner Mongolia University of Science and Technology, Baotou 014000, China;
2. Zhejiang Institute of Communications Collaborative Innovation Center for Application of Highway and Waterway Steel Bridge, Hangzhou 311112, China;
3. Shandong Road and Bridge Group, Jinan 250014, China;
4. Guizhou Engineering Research Center for Mountain-Area Expressway Intelligence Operation and Maintenance, Guiyang 550000, China

摘要

摘要: 钢桥高强度螺栓松动、脱落已成为钢结构行业关注的热点问题。现有的人工检查方法费时、费力且效率较低。机器视觉用于钢桥高强度螺栓松动检测,可大幅度提高检测效率,降低检测成本,确保检测精度。但这种方法在螺栓识别与检测可靠性方面还有待改进,尚未应用于工程实际。针对钢桥高强度螺栓松动角度检测的需求,提出一种基于沙漏网络和数字图像处理的高强度螺栓松动检测算法,算法包含特征点识别与角度计算两部分。识别算法由关键点定位网络和关键点坐标生成模块两个部分组成。关键点定位网络为ResNet-50特征提取网络,旨在生成包含螺栓关键点的热力图;关键点坐标生成模块基于热力图,采用定位网络检测出关键点坐标。松动角度算法为:获得关键点热力图后,通过K-Means聚类算法将螺栓关键点转化为包络图;获得螺栓包络图之后,依次逆时针对6个角点进行编号;并将角点1与x轴正向的夹角作为螺栓当前角度,通过不同时间相应角度的对比,得到螺栓的松动角度。采集了多座公路钢桥的高强度螺栓连接节点照片,标注了螺母外侧6个角点和中心点,用几何变换、颜色变换、添加粗放式丢弃(Coarse Dropout)噪声、颜色扰动等操作,扩充了螺栓数据集。基于TensorFlow深度学习框架进行了模型训练,测试了不同距离下模型的识别效果,并将网络识别结果与CPMs和Two-Stage网络进行了对比。测试表明,该网络模型的识别效果好,计算效率高,且具有很好的鲁棒性。其中A_PCK和A_ACC高达 97.6%和99.5%,A_PCK和A_ACC检测速度均优于CPMs和Two-Stage网络。
- 螺栓松动检测 /
- 全卷积神经网络 /
- 深度学习 /
- 图像处理
Abstract: The loosening and detachment of high-strength bolts in steel bridges have become a hot topic in the steel structure industry. The existing manual inspection methods are laborious, time-consuming, and inefficient. Machine vision is used for detecting loose high-strength bolts in steel bridges, which can greatly improve detection efficiency, reduce detection costs, and ensure detection accuracy. However, this method still needs improvement in terms of bolt recognition and detection reliability, and has not yet been applied in engineering practice. To meet the demand for detecting the loosening angle of high-strength bolts in steel bridges, a high-strength bolt loosening detection algorithm based on the Hourglass network and digital image processing has been proposed. The algorithm consists of two parts: feature point recognition and angle calculation. The recognition algorithm consists of two parts: the key point localization network and the key point coordinate generation module. The key point localization network is a ResNet-50 feature extraction network designed to generate heatmaps containing the key points of the bolts. The key point coordinate generation module, based on the heatmaps, uses the localization network to detect the coordinates of the key points. The loosening angle algorithm works as follows: after obtaining the key point heatmaps, the bolt key points are transformed into an envelope diagram using the K-Means clustering algorithm. Once the bolt envelope diagram is obtained, the six corner points are numbered sequentially in a counterclockwise manner. The angle between corner point 1 and the positive x-axis is taken as the current angle of the bolt. By comparing the current angles at different times, the loosening angle of the bolt can be determined. Multiple photos of high-strength bolt connections on highway steel bridges have been collected, with the outer six corner points and center point of the nuts labeled. The bolt dataset has been expanded by applying geometric transformations, color transformations, adding Coarse Dropout noise, color perturbations, and other operations. A model training was conducted using the TensorFlow deep learning framework, and the recognition performance of the model at different distances was tested. The recognition results of the network in this paper were compared with CPMs and Two-Stage networks. The tests showed that the network model in this paper had excellent recognition performance, high computational efficiency, and good robustness. The A_PCK and A_ACC were as high as 97.6% and 99.5%, respectively. The A_PCK, A_ACC, and detection speed were superior to CPMs and Two-Stage networks.
- bolt loosening detection /
- fully convolutional neural network /
- deep learning /
- image processing

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参考文献(14)

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