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

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Alexandre Almeida Del Savio , Ana Luna Torres , Daniel Cárdenas-Salas , Mónica Vergara Olivera , Gianella Urday Ibarra
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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.
用于建筑工地监控和神经网络训练的倾斜和站立人员图像人工分类数据集
这篇数据论文展示了一个人工标记的数据集,其中包括使用四个静态摄像机从建筑工地捕获的1,214张人员图像。有两类,站立和倾斜的人。分类存储在附带的文本文件和每个图像的边界框坐标中。该编译是为了支持开发和验证用于建筑工地监测的计算机视觉和人工智能模型。该数据集解决了在复杂的建筑环境中寻找不同姿势的人员的挑战。该资源将通过允许更精确的神经网络训练来增强施工现场安全监测和人员活动分析。该数据集存储在一个公共存储库中,使其在计算机视觉、土木工程和工作场所安全方面的学术和工业用途上可以公开访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Data in Brief
Data in Brief MULTIDISCIPLINARY SCIENCES-
CiteScore
3.10
自引率
0.00%
发文量
996
审稿时长
70 days
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.
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