Abstract: A method for remote identification of security threats in an imaged object including transmitting an initialization signal to a first threat detection scanner over a communication network, the first threat detection scanner being located at a separate physical location, receiving a ready-to-send signal from the first threat detection scanner, the ready-to-send signal including a storage location of a scan image generated by the first threat detection scanner for security inspection, receiving the scan image from the first threat detection scanner, transmitting, after receiving the scan image from the first threat detection scanner, a second initialization signal to a second threat detection scanner at the separate physical location, generating a threat detection report based on a rendering of the scan image, and transmitting the threat detection report to the first threat detection scanner.

Abstract: 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.

Abstract: A method for remote identification of security threats in an imaged object including transmitting an initialization signal to a first threat detection scanner over a communication network, the first threat detection scanner being located at a separate physical location, receiving a ready-to-send signal from the first threat detection scanner, the ready-to-send signal including a storage location of a scan image generated by the first threat detection scanner for security inspection, receiving the scan image from the first threat detection scanner, transmitting, after receiving the scan image from the first threat detection scanner, a second initialization signal to a second threat detection scanner at the separate physical location, generating a threat detection report based on a rendering of the scan image, and transmitting the threat detection report to the first threat detection scanner.

Abstract: A method for remote identification of security threats in an imaged object including transmitting an initialization signal to a first threat detection scanner over a communication network, the first threat detection scanner being located at a separate physical location, receiving a ready-to-send signal from the first threat detection scanner, the ready-to-send signal including a storage location of a scan image generated by the first threat detection scanner for security inspection, receiving the scan image from the first threat detection scanner, transmitting, after receiving the scan image from the first threat detection scanner, a second initialization signal to a second threat detection scanner at the separate physical location, generating a threat detection report based on a rendering of the scan image, and transmitting the threat detection report to the first threat detection scanner.

Abstract: A method for remote identification of security threats in an imaged object including transmitting an initialization signal to a first threat detection scanner over a communication network, the first threat detection scanner being located at a separate physical location, receiving a ready-to-send signal from the first threat detection scanner, the ready-to-send signal including a storage location of a scan image generated by the first threat detection scanner for security inspection, receiving the scan image from the first threat detection scanner, transmitting, after receiving the scan image from the first threat detection scanner, a second initialization signal to a second threat detection scanner at the separate physical location, generating a threat detection report based on a rendering of the scan image, and transmitting the threat detection report to the first threat detection scanner.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.