TY - JOUR
T1 - Dataset of manually classified images obtained from a construction site
AU - Savio, Alexandre Almeida Del
AU - ANA, FELICITA LUNA TORRES
AU - Cardenas, Daniel
AU - Vergara, Mónica
AU - Ibarra, Gianella Urday
N1 - Funding Information:
The authors wish to thank Universidad de Lima for the use of equipment and image acquisition. This work was supported and funded by the Institute of Scientific Research of the University of Lima (IDIC) with Project Number PI.71.002.2020.
Publisher Copyright:
© 2022
PY - 2022/6
Y1 - 2022/6
N2 - A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset consists of 1046 images selected from video footage by a frame extraction algorithm and txt files containing the objects' class and coordinates information. These data can be used to develop computer vision techniques in the engineering and construction fields.
AB - A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset consists of 1046 images selected from video footage by a frame extraction algorithm and txt files containing the objects' class and coordinates information. These data can be used to develop computer vision techniques in the engineering and construction fields.
KW - Construction images
KW - neural network training images
KW - construction monitoring images
KW - computer vision image classification
KW - Construction objects
UR - https://hdl.handle.net/20.500.12724/17770
UR - https://doi.org/10.1016/j.dib.2022.108042
UR - http://www.scopus.com/inward/record.url?scp=85126605198&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/8c2c169d-72f4-3b4a-9479-12bee69b7ce0/
U2 - 10.1016/j.dib.2022.108042
DO - 10.1016/j.dib.2022.108042
M3 - Artículo (Contribución a Revista)
VL - 42
JO - Data in Brief
JF - Data in Brief
M1 - 108042
ER -