The present specification discloses systems and methods for identifying and reporting contents of a tanker, container or vehicle. Programmatic tools are provided to assist an operator in analyzing contents of a tanker, container or vehicle. Manifest data is automatically imported into the system for each shipment, thereby helping security personnel to quickly determine container contents. In case of a mismatch between container contents shown by manifest data and the contents as ascertained from the scanning system, the container or vehicle may be withheld for further inspection.

In a transportation security technique, images are stored that are received from image capturing equipment deployed at respective screening nodes. The images are analyzed using a machine learning model, where presence of a particular object in an image indicates that a threat condition exists at the screening node. The analyzed images are transmitted to threat assessment components in accordance with predetermined criteria. An indication that the particular object is observed in the image is received from the threat assessment components. An indication that the particular object is observed in the image is transmitted to the screening node responsive to receiving the indication that the particular object is observed in the image. An indication of whether the particular object is present at the screening node is received. The machine learning model is trained based on the received indication of whether the particular object is observed in the image.

The present application is directed toward cargo scanning systems having scanners, each arranged to scan a respective object and generate a set of scan data, processors arranged to process each set of scan data to determine whether it meets a predetermined threat condition, workstations, and data management system arranged to direct data that meets the threat condition to one of the workstations for analysis.

The present specification discloses systems and methods for integrating manifest data for cargo and light vehicles with their X-ray images generated during scanning. Manifest data is automatically imported into the system for each shipment, and helps the security personnel to quickly determine the contents of cargo. In case of a mismatch between cargo contents shown by manifest data and the X-ray images, the cargo may be withheld for further inspection. In one embodiment, the process of analyzing the X-ray image of the cargo in conjunction with the manifest data is automated.

The present specification discloses systems and methods for integrating manifest data for cargo and light vehicles with their X-ray images generated during scanning. Manifest data is automatically imported into the system for each shipment, and helps the security personnel to quickly determine the contents of cargo. In case of a mismatch between cargo contents shown by manifest data and the X-ray images, the cargo may be withheld for further inspection. In one embodiment, the process of analyzing the X-ray image of the cargo in conjunction with the manifest data is automated.

A three-dimensional (3D) x-ray tomographic imaging system includes an x-ray source fixedly attached to a first unmanned vehicle, which can be aerial or otherwise configured for locomotion, and an x-ray detector. A vehicle controller is configured to be operated by an operator, and an optical camera is mounted to the first unmanned vehicle at a fixed position relative to the x-ray source, and an optical pattern is fixed at a position relative to the x-ray detector. The x-ray source and x-ray detector are configured to be positioned on substantially opposite sides of the object, while the x-ray source is rotated radially around the object to one or more imaging positions.

The present specification discloses systems and methods for integrating manifest data for cargo and light vehicles with their X-ray images generated during scanning. Manifest data is automatically imported into the system for each shipment, and helps the security personnel to quickly determine the contents of cargo. In case of a mismatch between cargo contents shown by manifest data and the X-ray images, the cargo may be withheld for further inspection. In one embodiment, the process of analyzing the X-ray image of the cargo in conjunction with the manifest data is automated.

The present application is directed toward cargo scanning systems having scanners, each arranged to scan a respective object and generate a set of scan data, processors arranged to process each set of scan data to determine whether it meets a predetermined threat condition, workstations, and data management system arranged to direct data that meets the threat condition to one of the workstations for analysis.

An x-ray screening system includes a plurality of x-ray screening devices each for scanning at least one object of interest. Each screening device emits x-rays which pass through the object of interest and which are detected by a group of detectors including at least one detector to provide measured x-ray energy signals. At least one central processor is in data communication with each screening device for receiving the measured x-ray energy signals from each screening device automatically and in real-time. The at least one processor automatically analyzes the measured x-ray energy signals in real-time to determine at least one property of the object of interest and determining whether the at least one property indicates that at least a portion of the object of interest is composed of a material of interest. The material of interest may be a potentially dangerous material.

Provided herein are threat source mapping systems and related techniques. A threat source mapping system includes a threat sensor network and a logic device. The threat sensor network includes one or more threat detectors each configured to monitor at least a portion of a scene for at least one threat detection event. The logic device is configured to receive the at least one threat detection event from the threat sensor network, generate a threat source location heat map based, at least in part, on the at least one threat detection event, and generate a threat source image map based, at least in part, on the threat source location heat map and at least one image of the scene that at least partially overlaps the portions of the scene monitored by the one or more threat detectors. The threat source image map may then be displayed to a user.