Air Cargo skids and pallets take up a large amount of space on every commercial passenger flight. Federal law requires this cargo be screened at the same level as checked baggage. Single and dual energy X-ray systems can be scaled up to screen air cargo skids, but they produce two-dimensional views of air cargo that are often difficult for screeners to interpret due to the complexity of the content’s image. So, many skids are broken down into smaller configurations or single packages and screened individually or with trace explosive detection swabs similar to those used in passenger screening lines.
Increases in computing power, algorithmic complexity, and machine learning capabilities offer opportunities to enhance current X-ray screening capabilities and reduce the amount of time to break down and reassemble transported cargo. The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) and the Transportation Security Administration (TSA) are collaborating to address these challenges. S&T funded development of the Opacity and Complexity Analysis Software Tool (OCAST), a support algorithm to assist X-ray operators in determining possible threats in cargo and which areas pose no threat, despite complex X-ray images.
“TSA is interested in augmenting existing systems to improve their performance and reduce burdens on the operators,” said DHS S&T Program Manager for Air Cargo Kumar Babu. “OCAST does this with powerful image processing software. It is essentially an automatic algorithm that analyzes an image and provides an operator a description of the complexity of the image. The operator can use that score plus their own view of the image to determine whether to pass the cargo or investigate further.”
