Automatic Optical & Laser-based Defect Detection and Classification in Brick Masonry Walls

被引：0

作者：

Samy, Meena Periya ^{[1
]}

Foong, Shaohui ^{[2
]}

Soh, Gim Song ^{[2
]}

Yeo, Kang Shua ^{[3
]}

机构：

[1] SUTD, SUTD Digital Mfg & Design Ctr, Singapore, Singapore

[2] SUTD, Engn Prod Dev Pillar, Singapore, Singapore

[3] SUTD, Architecture & Sustainable Design Pillar, Singapore, Singapore

来源：

PROCEEDINGS OF THE 2016 IEEE REGION 10 CONFERENCE (TENCON) | 2016年

关键词：

Masonry Defects; SVM; Defect detection; Classification;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A real time system fusing data from vision and laser sensors to detect and classify types of defects in brick masonry is presented. A Support Vector Machine (SVM) algorithm is conceived and used to develop a Defect Finding Classification Model (DFCM) to automatically classify the types of defects found in masonry walls using the image data obtained from both vision and 2D laser sensors mounted on an articulated 6-axis robotic arm. Thirteen image features were extracted to train the SVM. It was found that the proposed approach has a detection accuracy of over 96%.

引用

页码：3521 / 3524

页数：4

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