Abstract: A system and method for automatically detecting prohibited materials in a compartment at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, and classifying each voxel of the three-dimensional representation using a trained neural network to determine whether any voxel classifications of the three-dimensional representation correspond to a voxel classification of a prohibited material.

Abstract: A system and method for automatically detecting prohibited objects in a compartment at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, identifying, by the processor, a region within the compartment that has a highest likelihood of containing a prohibited object, based on a voxel classification of the three-dimensional representation using a first trained neural network, and classifying, by the processor, shapes of objects located within the identified region using a second trained neural network to determine whether any of the shapes correspond to a shape of a prohibited object known by the second neural network.

Abstract: A system and method for automatically classifying compartments at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, and classifying the compartment into a first category or a second category using a trained neural network that analyzes the three-dimensional representation, wherein compartments classified into the first category are automatically cleared at the security checkpoint and compartments classified into the second category undergo further security checks.

Abstract: A system and method for automatically detecting prohibited materials in a compartment at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, and classifying each voxel of the three-dimensional representation using a trained neural network to determine whether any voxel classifications of the three-dimensional representation correspond to a voxel classification of a prohibited material.

Abstract: A system and method for automatically detecting prohibited objects in a compartment at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, identifying, by the processor, a region within the compartment that has a highest likelihood of containing a prohibited object, based on a voxel classification of the three-dimensional representation using a first trained neural network, and classifying, by the processor, shapes of objects located within the identified region using a second trained neural network to determine whether any of the shapes correspond to a shape of a prohibited object known by the second neural network.

Abstract: A system and method for automatically classifying compartments at a security checkpoint includes receiving a three-dimensional representation of a compartment from an imaging device connected to the computing system, and classifying the compartment into a first category or a second category using a trained neural network that analyzes the three-dimensional representation, wherein compartments classified into the first category are automatically cleared at the security checkpoint and compartments classified into the second category undergo further security checks.