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What is ACID?

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Features​

  • Support object detection, instance segmentation and image captioning tasks in infrastructure construction

  • 10 categories of construction machines

  • 10,000 labeled images 

  • 2,904 on-site videos

  • 15,767 construction machine objects

  • 19,547 captioning sentences

ACID, the Advanced Construction Image Dataset (formerly known as the Alberta Construction Image Dataset), is a community-driven open-sourced dataset that provides high-quality training resources and examples for applying artificial intelligence (AI) in infrastructure construction. Currently, ACID supports three construction AI tasks including object detection, instance segmentation, and image captioning, along with corresponding data, annotations, guidelines, and algorithm analysis results. The overarching goal of ACID is to foster an open data-driven and self-sustaining open-source ecosystem for construction and infrastructure management.

 Dataset image examples

Construction Applications Powered by ACID

AI algorithms can automatically identify construction objects/instances/scenes in images and videos through parallel computation and graphic card implementation. However, they require large-scale datasets to mitigate the overfitting problem during the training stage. Since construction sites are often challenging to access, creating extensive datasets containing high-quality construction data poses a significant challenge. The community faces a critical barrier, which is the lack of standardized, high-quality, and domain-specific open data resources. This fragmentation undermines reproducibility, slows innovation, and prevents the construction community from developing benchmark tasks that can drive rapid progress 

 

ACID dataset was developed as one of the first pioneering open-source artifacts in construction and infrastructure management community. Its novelty lies in providing the community with: 1) infrastructure construction-specific AI research and education resources; 2) benchmark tasks and algorithm analysis to ensure reproducibility and reusability of construction AI research; and 3) annotation guidelines to standardize the creation of future datasets. Through years efforts, ACID has established a transformative foundation for construction AI. The following videos showcase various AI applications backboned by ACID.

Download and Cite ACID Paper

Two papers have been published with ASCE for ACID. If you find ACID is helpful to your work, please consider citing our papers:

 

Dataset Development and Object detection (download link)

Xiao, B., & Kang, S. C. (2021). Development of an image data set of construction machines for deep learning object detection. Journal of Computing in Civil Engineering, 35(2), 05020005.

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Image Captioning (download link)

Xiao, B., Wang, Y., & Kang, S. C. (2022). Deep learning image captioning in construction management: a feasibility study. Journal of Construction Engineering and Management, 148(7), 04022049.

Impacts of ACID

Until now, ACID has supported over 900 researchers from universities/companies of 30 different countries.  

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ACID users come from diverse backgrounds and research communities, such as construction and infrastructure management, computer science, civil engineering, and etc. Below shows the ACID user demographic. 

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ACID Statements

ACID Dataset

ACID provides image dataset, guidelines, and algorithm analysis results for training AI models in construction automation. The development of ACID has experienced several stages and various researchers have contributed to it (see here for development roadmap of ACID). The intellectual property of ACID belongs to the ACID group, which is not affiliated to any specific university/institution. 

ACID Group

ACID group is a group of researchers from North America, Asia, and Australia (see ACID people) working together to push the boundaries of AI in Construction. The ACID group is currently taking the responsibility to maintain and develop ACID. 

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