Manually classified dataset of leaning and standing personnel images for construction site monitoring and neural network training

• 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: 2025
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/23193
• Enlace del recurso: https://hdl.handle.net/20.500.12724/23193; https://doi.org/10.1016/j.dib.2025.111516
• Nivel de acceso: acceso abierto
• Materia: Pendiente

This data paper presents a manually labeled dataset of 1,214 images of personnel captured from a construction site using four static cameras. There are two classes, standing and people leaning. The classification is stored in accompanying text files and bounding box coordinates for every image. The compilation was done to support the developing and validation computer vision and AI models for construction site monitoring. This dataset addresses the challenges of finding personnel in different poses within complex construction environments. The resource will enhance construction site safety monitoring and personnel activity analysis by allowing more precise neural network training. The dataset is stored in a public repository, making it openly accessible for academic and industrial purposes regarding computer vision, civil engineering, and workplace safety.