characteristic selection, crack recognition, distribution and utilization, RBF SVM,"/> 基于机器视觉检测的裂缝特征研究
 
 
土木工程学报  2016, Vol. 49 Issue (7): 123-128    
  结构工程 本期目录 | 过刊浏览 | 高级检索 |
基于机器视觉检测的裂缝特征研究
王 睿 漆泰岳
1. 西南交通大学交通隧道工程教育部重点实验室, 四川成都  610031;
2. 西南交通大学, 四川成都  610031
Study on crack characteristics based on machine vision detection#br#
Wang Rui Qi Taiyue
1. Key Laboratory of Transportation Tunnel Engineering of the Ministry of Education, Southwest Jiaotong University, Chengdu 610031, China;
2. Southwest Jiaotong University, Chengdu 610031, China
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摘要 
裂缝作为土木工程中常见的病害,随着基于机器视觉检测设备的不断优化,如何对裂缝
进行高效的识别与统计是整个系统高效运行的关键。然而这两者均涉及的一点就是特征选取。
初选定特征后,如何对特征进行合理的分配应用是个需要探讨的问题。本文围绕裂缝特征及其
应用展开,在选定裂缝特征之后,首先对应用部分裂缝特征的有效性进行了验证,即通过对处
理得到的两值图像进行初筛选滤去大部分杂质;其后根据RBF-SVM算法建立自动判别模型,分别
将6个特征(全部)和3个特征(部分)作为输入参数的工况进行结果对比,表明该模型具有很好的
适应性,且均能高效实现裂缝识别,进而验证了裂缝特征分配应用的必要性。
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王 睿 漆泰岳
关键词 特征选取 裂缝识别 分配应用 RBF-SVM    
Abstract
As the common defects, cracks are critical to the performance of civil
engineering structures. With the continuous optimization of inspection system based
on machine vision, the effective crack recognition and statistics are developed and
become the key of the whole system, in which selection of characteristics is
involved. After preliminary characteristics selection, the reasonable distribution
and utilization of the characteristics should be further studied. After selecting
the crack characteristics, the validity of crack characteristics application is
verified by filtering a large number of impurities from the processed binary
images. And then,  an automated identification model is established according to
the RBF-SVM algorithm. 6 characteristics (all) and 3 characteristics (part) are
used as  the input parameters, respectively. It shows that this model has good
adaptability and high-efficiency in crack identification, which further validates
the necessity of the distribution and utilization of crack characteristics.
Key wordscharacteristic selection')" href="#">
    
引用本文:   
王 睿 漆泰岳. 基于机器视觉检测的裂缝特征研究[J]. 土木工程学报, 2016, 49(7): 123-128.
Wang Rui Qi Taiyue. Study on crack characteristics based on machine vision detection#br#. 土木工程学报, 2016, 49(7): 123-128.
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