Automated Brick Counting for Façade Construction Progress Estimation

AbstractConstruction progress is predominantly measured with manual site surveys. These surveys are labor-intensive, on-site manual investigations. The generated survey reports are subjective and approximate because they are based on the surveyors’ individual experiences. This paper presents a novel method that can automatically count the number of bricks on a facade for reducing the cost and increasing the reliability of progress surveys. The method uses video data taken from a user’s mobile phone to detect bricks on a facade in each video frame by using color thresholding, edge detection, and filtering of rectangular shapes and sizes. Then, it compares the difference between consecutive frames to add counts when new bricks appear and to avoid double counting. The proposed method was implemented and tested on on-site videos of red brick facades, and resulted in 99.8% precision and 98.7% recall. The results demonstrate the suitability of this method for progress monitoring of brick facade construction.

[1]  N. Mansfield,et al.  Causes of delay and cost overruns in Nigerian construction projects , 1994 .

[2]  X. Zhang,et al.  Towards automated progress assessment of workpackage components in construction projects using computer vision , 2009, Adv. Eng. Informatics.

[3]  Lucio Soibelman,et al.  Material Waste in Building Industry: Main Causes and Prevention , 2002 .

[4]  Tom Drummond,et al.  Fusing points and lines for high performance tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Rafael Sacks,et al.  Assessing research issues in Automated Project Performance Control (APPC) , 2007 .

[6]  Fei Dai,et al.  Comparison of Image-Based and Time-of-Flight-Based Technologies for Three-Dimensional Reconstruction of Infrastructure , 2013 .

[7]  Youssef M A Hashash,et al.  Integration of Construction As-Built Data Via Laser Scanning with Geotechnical Monitoring of Urban Excavation , 2006 .

[8]  Frédéric Bosché,et al.  Automated progress tracking using 4D schedule and 3D sensing technologies , 2012 .

[9]  Jun Cheng,et al.  Size Detection of Firebricks Based on Machine Vision Technology , 2010, 2010 International Conference on Measuring Technology and Mechatronics Automation.

[10]  Chris Chatwin,et al.  Image Processing Applied to Brick Quality Control , 2000 .

[11]  Carlos Hernández,et al.  Video-based, real-time multi-view stereo , 2011, Image Vis. Comput..

[12]  Mani Golparvar-Fard,et al.  Visualization of construction progress monitoring with 4D simulation model overlaid on time-lapsed photographs , 2009 .

[13]  Frédéric Bosché,et al.  Automated retrieval of 3D CAD model objects in construction range images , 2008 .

[14]  Burcu Akinci,et al.  Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST , 2010 .

[15]  Ioannis Brilakis,et al.  Progressive 3D reconstruction of infrastructure with videogrammetry , 2011 .

[16]  M. Emre Celebi Real-Time Implementation of Order-Statistics Based Directional Filters , 2009, IET Image Process..

[17]  Marc Pollefeys,et al.  Live Metric 3D Reconstruction on Mobile Phones , 2013, 2013 IEEE International Conference on Computer Vision.

[18]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.