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

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.

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Publication Details

Journal
Data in Brief
Published
2025-03-24
DOI
https://doi.org/10.1016/j.dib.2025.111516
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Gianella Urday Ibarra, Mónica Alejandra Vergara Olivera, Ana Luna Torres, Daniel Cárdenas-Salas et al.
Data in Brief
Infrastructure Maintenance and Monitoring
article

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

Gianella Urday Ibarra, Mónica Alejandra Vergara Olivera, Ana Luna Torres, Daniel Cárdenas-Salas, Alexandre Almeida Del Savio
article en

Abstract

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.

Data in BriefVol. 60
Universidad de Lima (PE), Universidad Científica del Sur (PE), Carleton University (CA)
Openalex Percentile: Top 94%
Infrastructure Maintenance and Monitoring
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