Alexandre Almeida Del Savio , Ana Luna Torres , Daniel Cárdenas-Salas , Mónica Vergara Olivera , Gianella Urday Ibarra
{"title":"Manually classified dataset of leaning and standing personnel images for construction site monitoring and neural network training","authors":"Alexandre Almeida Del Savio , Ana Luna Torres , Daniel Cárdenas-Salas , Mónica Vergara Olivera , Gianella Urday Ibarra","doi":"10.1016/j.dib.2025.111516","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"60 ","pages":"Article 111516"},"PeriodicalIF":1.0000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352340925002483","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.