The International Association for Automation and Robotics in Construction
Publications / 2025 Proceedings of the 42nd ISARC, Montreal, Canada
Construction Industry Vision Alberta Dataset (CIVAD): Developing a Comprehensive Object Detection Dataset for Diverse Construction Applications
Integrating computer vision technologies into the 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.
