Dataset of manually classified images obtained from a construction site

• Autores: Del Savio, Alexandre Almeida, Luna Torres, Ana Felícita, Cárdenas Salas, Daniel Enrique, Vergara Olivera, Mónica Alejandra, Urday Ibarra, Gianella Tania
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/17770
• Enlace del recurso: https://hdl.handle.net/20.500.12724/17770; https://doi.org/10.1016/j.dib.2022.108042
• Nivel de acceso: acceso abierto
• Materia: Building; Neural networks (Computer science); Pattern perception; Image processing; Construcción; Redes neuronales (Informática); Reconocimiento de formas; Proceso de imágenes; https://purl.org/pe-repo/ocde/ford#2.01.03

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.