Logo image
Back
Automatic optical & laser-based defect detection and classification in brick masonry walls
Conference proceeding

Automatic optical & laser-based defect detection and classification in brick masonry walls

Meena Periya Samy, Shaohui Foong, Gim Song Soh and Kang Shua Yeo
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, p.3521
01/01/2016

Abstract

Algorithms Bricks Brickwork Defects Feature extraction Image classification Laser applications Robot arms Sensors Support vector machines Walls
Conference Title: TENCON 2016 - 2016 IEEE Region 10 Conference Conference Start Date: 2016, Nov. 22 Conference End Date: 2016, Nov. 25 Conference Location: Singapore 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%.

Metrics

1 Record Views

Details

Logo image