摘要
由于地震灾害发生后建筑物表面具有多纹理性、多目标性特征,导致现有的建筑物裂缝智能检测方法已经不能满足检测需要。为了提高检测效果,该文设计提出基于特征显著性的地震灾害后建筑物裂缝智能检测方法。建立压缩感知去噪框架,通过图像重构消除震后建筑裂缝图像噪点。采用FCM聚类分割法对去噪图像进行分割,引入灰度直方图作为灰度级的模糊聚类样本点,利用灰度样本完成图像聚类。基于人眼视觉特征,对图像背景区域重新划分,完成图像边缘检测。基于提取的二值化图像确定裂纹特征,根据特征值范围确定裂缝种类实现震后建筑裂缝检测。
Due to the multi-texture and multi-target characteristics of building surface after earthquake disaster, the existing intelligent detection methods for building cracks can not meet the needs of detection.In order to improve the detection effect, an intelligent crack detection method based on feature significance is proposed.The compressed sensing de-noising framework is established to eliminate the noise points of post-earthquake building cracks through image reconstruction.FCM clustering segmentation method was used to segment de-noising images, gray histogram was introduced as gray level fuzzy clustering sample point, and gray scale sample was used to complete image clustering.Based on the visual characteristics of human eyes, the background area of the image was reclassified to complete the image edge detection.Based on the extracted binarization image, the crack characteristics are determined, and the crack types are determined according to the range of characteristic values to realize the post-earthquake building crack detection.Compared with the experimental data, it is proved that the intelligent detection model of building cracks designed above can improve the detection rate of image edge by 29% and the detection rate of image gray scale by 23%, and the overall detection effect is excellent.
作者
史晓东
刘洋
SHI Xiaodong;LIU Yang(School of E-commerce and Logistics Management,Henan University of Economics and Law,ZhengZhou 450016,China;Modern Educational Technology Center,Henan University of Economics and Law,ZhengZhou 450016,China)
出处
《灾害学》
CSCD
北大核心
2020年第4期38-42,共5页
Journal of Catastrophology
基金
河南省科技攻关项目(202102210140,182102210021)
河南省高等学校重点科研项目(17A520020,18A520014)。
关键词
特征显著性
智能检测
图像噪点
模糊聚类
地震灾害后建筑
裂缝
characteristic significance
intelligent detection
image noise
fuzzy clustering
construction after earthquake disaster
cracks
