학술논문
基于高光谱成像的桥梁混凝土表面露筋病害识别 / Identification of Exposed Reinforcement Defects in Bridge Concrete Based on Hyperspectral Imaging
Document Type
Academic Journal
Author
Source
红外技术 / Infrared Technology. 46(2):216-224
Subject
Language
Chinese
ISSN
1001-8891
Abstract
桥梁作为交通关键节点,承担与日俱增的交通流量压力,相当一部分桥梁尚未达到设计使用年限就出现较多的病害,技术状况不容乐观.高光谱成像运用光电技术检测物体对光谱波段信号的辐射和吸收情况,将该信号转换成图像和图形,可基于吸收峰的位置和强度分析被测物体的物理性质和物质组成,因此本文提出基于高光谱成像的桥梁混凝土表面露筋病害识别方法.利用线阵高光谱相机集成匀速步进滑轨装置,形成高光谱成像测试系统,采集桥梁混凝土表面露筋病害图像;基于桥梁露筋病害高光谱图像谱线与空间特征,结合预处理——平滑滤波-多元散射校准(Savizky-Golay-Multivariate scattering calibration,SG-MSC)、特征空间变换——光谱导数法(First derivative,FD)、特征变量选择算法——竞争自适应重加权抽样(Competitive adapative reweighted sampling,CARS),将原始光谱曲线数据经特征空间转换提取相应特征值并显示波段;以光谱曲线特征向量构建数据集,基于支持向量机形成露筋病害识别预测模型.以某跨江大桥为例,以高光谱成像测试系统对实际桥梁混凝土露筋病害进行识别,将原始光谱数据经平滑特征空间变换与特征提取后放大差异,将 254个波段数据维度降低到 23个波段数据,模型预测精度达到 94.6%,对比可见光成像高光谱成像具有更高维度信息可有效表征物质属性,表明高光谱成像对复杂表面环境下的桥梁病害识别具有可行性和广泛应用前景.
As a key mode of transportation,bridges bear the high pressure of traffic flow.Many bridges have defects before reaching their designed service life.Bridge-defect recognition based on visible light uses grayscale defect images and regional edge gradient information,which have limitations in complex environments.The radiation and absorption of spectral band signals by objects are detected by hyperspectral imaging,and the signals are transformed into images and graphics.The physical properties of the measured object are analyzed based on the position and intensity of the absorption peak.In this study,a method based on hyperspectral vision is proposed to identify exposed reinforcement bar defects in bridge concrete.Based on the spectral lines and spatial features of hyperspectral images of exposed reinforcement defects in bridge concrete combined with processing——Smooth filtering multivariate scattering calibration(SG-MSC),feature space transformation——First derivative method(FD),and feature variable selection algorithm——Competitive adapative reweighted sampling(CARS),the original spectral curve data were transformed into feature space to extract the corresponding feature values and display the band.The dataset was constructed based on spectral curve feature vectors,and a support vector machine algorithm was used to establish a prediction model for identifying exposed reinforcement defects.Considering a cross-river bridge as an example,a hyperspectral visual testing system was used to identify actual exposed reinforcement bar defects of the bridge.By performing smooth feature space transformation and feature extraction on the original spectral data,the differences were amplified,reducing the dimensionality of the 254 band data to 23 band data and achieving a model prediction accuracy of 94.6%.Hyperspectral vision has higher dimensional information than visible-light vision.Hence,the proposed model can effectively characterize material properties,is feasible,and has broad application prospects.
As a key mode of transportation,bridges bear the high pressure of traffic flow.Many bridges have defects before reaching their designed service life.Bridge-defect recognition based on visible light uses grayscale defect images and regional edge gradient information,which have limitations in complex environments.The radiation and absorption of spectral band signals by objects are detected by hyperspectral imaging,and the signals are transformed into images and graphics.The physical properties of the measured object are analyzed based on the position and intensity of the absorption peak.In this study,a method based on hyperspectral vision is proposed to identify exposed reinforcement bar defects in bridge concrete.Based on the spectral lines and spatial features of hyperspectral images of exposed reinforcement defects in bridge concrete combined with processing——Smooth filtering multivariate scattering calibration(SG-MSC),feature space transformation——First derivative method(FD),and feature variable selection algorithm——Competitive adapative reweighted sampling(CARS),the original spectral curve data were transformed into feature space to extract the corresponding feature values and display the band.The dataset was constructed based on spectral curve feature vectors,and a support vector machine algorithm was used to establish a prediction model for identifying exposed reinforcement defects.Considering a cross-river bridge as an example,a hyperspectral visual testing system was used to identify actual exposed reinforcement bar defects of the bridge.By performing smooth feature space transformation and feature extraction on the original spectral data,the differences were amplified,reducing the dimensionality of the 254 band data to 23 band data and achieving a model prediction accuracy of 94.6%.Hyperspectral vision has higher dimensional information than visible-light vision.Hence,the proposed model can effectively characterize material properties,is feasible,and has broad application prospects.