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Automatic threat recognition of prohibited items at aviation checkpoint . . . Using convolutional object detection algorithms trained on annotated x-ray images, we show real-time detection of prohibited items in carry-on luggage Results of the work so far indicate that this approach can detect selected prohibited items with high accuracy and minimal impact on operational false alarm rates
[1912. 06329] Toward Automatic Threat Recognition for Airport X-ray . . . At these checkpoints, the TSA employs a fleet of X-ray scanners, such as the Rapiscan 620DV, so Transportation Security Officers (TSOs) can inspect the contents of carry-on possessions However, identifying and locating all potential threats can be a challenging task
News Release: DHS S T Awards $3. 5M to Improve X-Ray Detection . . . Capture LLC, of San Diego, CA was awarded $1,168,773 to develop an automated threat detection algorithm for improved detection of prohibited items such as guns and knives Capture will use a deep learning 3D convolutional neural network approach to enhance algorithm development
Prohibited Item Detection in Airport X-Ray Security Images via . . . In this paper, we focus on automatically recognizing and localizing prohibited items in airport X-ray security images A top-down attention mechanism is applied to enhance a CNN classifier to additionally locate the prohibited items
Automatic threat recognition of prohibited items at aviation checkpoint . . . Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: a deep learning approach In Anomaly Detection and Imaging with X-Rays (ADIX) III (Vol 10632, pp 1063203–1063203) International Society for Optics and Photonics
X-Ray Object Detection | John B. Sigman - johnsigman. com Automatic threat recognition of prohibited items at aviation checkpoint with x-ray imaging: a deep learning approach Kevin J Liang , Geert Heilmann , Christopher Gregory , and 6 more authors
Automatic threat recognition of prohibited items at aviation . . . Using convolutional object detection algorithms trained on annotated x-ray images, we show real-time detection of prohibited items in carry-on luggage Results of the work so far indicate that this approach can detect selected prohibited items with high accuracy and minimal impact on operational false alarm rates