ALERT | Awareness and Localization of Explosive-Related Threats

ALERT Datasets

Currently ALERT has two active datasets available to the research community that have developed out of transition tasks supported by DHS. The two which are currently available are the CT segmentation dataset and the Reconstruction Initiative dataset. If you would like to inquire about accessing this data, please complete our Dataset Request Form.

CT Datasets

ALERT has leveraged the advances of medical CT, and has contracted with a vendor to obtain representative datasets of packed luggage and reference objects. Contractual arrangements were made to scan luggage on a state-of-the-art medical CT scanner at the manufacturer’s factory.  Approximately 900 objects were fully segmented from 62 luggage datasets to span the spectrum of packing, density, arrangement, orientation, and size difficulty.

The purpose of the data we have procured is to provide security-like data to academic researchers, to discover and evaluate the present segmentation state-of-the-art, and to stimulate additional communication and research in the segmentation algorithm research community.*

If you would like to inquire about accessing this data, please complete our Dataset Request Form.

ALERT personnel performed the following steps:

1. Procured luggage and items to pack in them.
2. Labeled, photographed and cataloged the items.
3. Packed the suitcases
4. Created a database of items as packed into suitcases
5. Created video tapes of unpacking the luggage
6. Scanned the luggage at the vendor’s factory

The vendor performed the following steps:

1. Reconstructed the projection data corresponding to the scans of the luggage. The reconstructions were performed with offline reconstruction. The resulting resolution was approximately 3 mm FWHM.
2. Converted the images to DICOM format.

ALERT then performed the following additional steps.

1. Converted the DICOM images to TIFF files.
2. Used a network in Mevislab to semi-automatically outline the items in the scans. The resulting data was known as ground truth data, label images and AO images.
3. Divided the scans into four sets denoted: qualification, training, validation and evaluation.
4. Distributed the data per instructions from the leadership team.
5. Revised the data based on feedback from various stakeholders.

* Our data is available to researchers under a Non-Disclosure Agreement(NDA). The NDA states that the data is owned by ALERT. If you choose to receive this data, you will not receive or create SSI data. Any publications generated, including reports and presentations, must be reviewed and approved by the Northeastern University/ALERT Research Evaluation Advisory Panel (REAP) in advance of publication or dissemination.