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Construction Industry Vision Alberta Dataset (CIVAD): Developing a Comprehensive Object Detection Dataset for Diverse Construction Applications

2025· article· en· 0 citations· W4412690934 sur OpenAlex· 10.22260/isarc2025/0124

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Aucune affiliation canadienne. Une base fondée sur la seule affiliation (le devis habituel) n'aurait jamais vu ce travail. C'est l'un des travaux qui justifient l'inversion de la base.

Le tri à trois modèles

les 1 000 travaux triés →

Les trois modèles l'ont jugé hors champ.

strate : about_only · poids de sondage : 3321.24 (l'échantillon est stratifié ; tout taux calculé sans le poids est faux)
Claude Opus 4.8OUT
genre : infrastructure/announcement
porte sur le Canada: non
confiance: medium

Announcement of a computer vision training dataset for construction sites; a domain dataset, not research-system infrastructure.

GPT-5.6 (high)OUT
genre : empirical
porte sur le Canada: non
confiance: high

The work develops a construction computer-vision dataset rather than studying research infrastructure or practice.

Grok 4.5OUT
genre : empirical
porte sur le Canada: non
confiance: high

Construction-site computer-vision training dataset for industry monitoring, not research infrastructure as object.

Résumé

Integrating computer vision technologies intothe construction industry has the potential to revolutionize site monitoring, safety management, and quality control.However, a critical gap remains in the availability of specialized datasets tailored to construction sites' distinct conditions and complexities while including sufficient classes representing most items in construction sites.Existing Computer Vision (CV) models often rely on generic training datasets, which limit their effectiveness for specific construction-monitoring-related tasks.Consequently, there is a pressing need for comprehensive domain-specific datasets that can capture the full spectrum of construction-related objects and activities.This study addresses this gap by developing a foundational training dataset called the Construction Industry Vision Alberta Dataset (CIVAD), specifically designed for CV applications in the construction sector.Our dataset included over 50 classes with more than 86,905 images of different objects, such as tools, machinery, safety equipment, and construction materials, to support diverse CV tasks.It utilizes a combination of web scraping, inclusion of existing open-source datasets, and direct data captured from construction sites.A set of novel methods, such as semiauto-labeling with advanced models, such as Grounded SAM and Grounding DINO, were used with our custom algorithms.These models were utilized to process parts of the dataset imagery with humans in the loop.This approach facilitated an efficient and accurate dataset creation process.The CIVAD dataset and methods employed in this study represent a significant step forward in integrating CV technologies across various construction-related applications.

Conservé avec la notice de tri, où il sert de preuve aux étiquettes ci-dessus.

La notice

Revue
Proceedings of the ... ISARC
Thématique
Infrastructure Maintenance and Monitoring
Domaine
Engineering
Établissements canadiens
Organismes subventionnaires
Mots-clés
Computer scienceObject detectionConstruction industryArtificial intelligenceData scienceComputer visionConstruction engineeringEngineeringPattern recognition (psychology)
Résumé présent dans OpenAlex
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