TY - GEN
T1 - EFFICIENT VERTICAL OBJECT DETECTION IN LARGE HIGH-QUALITY POINT CLOUDS OF CONSTRUCTION SITES
AU - Vega, Miguel A.
AU - Braun, Alexander
AU - Bauer, Heiko
AU - Noichl, Florian
AU - Borrmann, André
N1 - Publisher Copyright:
© 2021, European Council on Computing in Construction (EC3). All rights reserved.
PY - 2021
Y1 - 2021
N2 - Even when adherence to project schedule is the most critical performance metric among project owners, still 53 % of typical construction projects exhibit schedule delays. To contribute to more efficient construction progress monitoring, this research proposes a method to detect the most common temporary object classes in large-scale laser scanner point clouds of construction sites. The proposed workflow includes a combination of several techniques: image processing over vertical projections, finding patterns in 3D detected contours, and performing checks over vertical cross-sections. A deep learning algorithm was leveraged to classify these cross-sections for the purpose of formwork detection. After applying the method on three real-world point clouds and testing with three object categories (cranes, scaffolds, and formwork), the results reveal that the process achieves average rates above 88 % for precision and recall and outstanding computational performance (1s to process 105 points). These metrics demonstrate the method’s capability to support the automatic segmentation of point clouds of construction sites.
AB - Even when adherence to project schedule is the most critical performance metric among project owners, still 53 % of typical construction projects exhibit schedule delays. To contribute to more efficient construction progress monitoring, this research proposes a method to detect the most common temporary object classes in large-scale laser scanner point clouds of construction sites. The proposed workflow includes a combination of several techniques: image processing over vertical projections, finding patterns in 3D detected contours, and performing checks over vertical cross-sections. A deep learning algorithm was leveraged to classify these cross-sections for the purpose of formwork detection. After applying the method on three real-world point clouds and testing with three object categories (cranes, scaffolds, and formwork), the results reveal that the process achieves average rates above 88 % for precision and recall and outstanding computational performance (1s to process 105 points). These metrics demonstrate the method’s capability to support the automatic segmentation of point clouds of construction sites.
UR - http://www.scopus.com/inward/record.url?scp=85177215956&partnerID=8YFLogxK
U2 - 10.35490/EC3.2021.156
DO - 10.35490/EC3.2021.156
M3 - Conference contribution
AN - SCOPUS:85177215956
SN - 9783907234549
T3 - Proceedings of the European Conference on Computing in Construction
SP - 148
EP - 157
BT - Proceedings of the 2021 European Conference on Computing in Construction
A2 - Hall, Daniel M.
A2 - Chassiakos, Athanasios
A2 - O'Donnell, James
A2 - Nikolic, Dragana
A2 - Xenides, Yiannis
PB - European Council on Computing in Construction (EC3)
T2 - European Conference on Computing in Construction, EC3 2021
Y2 - 26 July 2021 through 28 July 2021
ER -