Residential Construction Image Dataset Using CSI Divisions for Deep Learning-based Object Detection
To address this gap, we introduce the Residential Construction Dataset Using CSI Divisions (ResCD-CSI) image dataset, a manually labeled image benchmark organized according to the divisional structure defined by the Construction Specifications Institute (CSI).
ProofPaper ↗
Key points
- Accurate recognition of construction site elements is essential for the application of deep learning (DL) in the construction industry.
- Despite rapid advances in computer vision, the adoption of DL in construction has been limited by the lack of structured, publicly available datasets aligned with established industry standards.
- The dataset comprises high-resolution images collected from active residential construction sites across the United States, capturing diverse building components, materials, and equipment under real-world conditions.
- By aligning with the CSI divisional framework, ResCD-CSI establishes a standardized resource that bridges construction practice with computer vision research and supports the development of Artificial Intelligence-driven solutions for residential construction monitoring.
Sources (1)
- [1]Residential Construction Image Dataset Using CSI Divisions for Deep Learning-based Object DetectionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 09:12 PM
To address this gap, we introduce the Residential Construction Dataset Using CSI Divisions (ResCD-CSI) image dataset, a manually labeled image benchmark organized according to the divisional structure defined by the Construction Specifications Institute (CSI).
Accurate recognition of construction site elements is essential for the application of deep learning (DL) in the construction industry.
Extractive summary: sentences quoted from the sources.