Object detection in full-height human x-ray images

Abstract

Detecting hidden objects on a human body includes acquiring an incoming X-ray image of the human body passing through a penetrating X-ray scanner; generating additional images based on the incoming image by performing logarithmic or saliency transformations or contrasting of the incoming image; obtaining maps for all objects and known object classes, the maps show which pixels correspond to objects or to background, by passing the incoming and the additional images through a neural network with a deep Segnet-U-Net architecture optimized for overlapping object detection with long skip connections before each downsampling layer of the neural network; using the maps, identifying unknown objects in the incoming image by recognizing all objects/objects of known classes, excluding previously classified objects from the known classes from segmented non-anatomic areas; segmenting the incoming image of the human body into multiple parts; and identifying parts containing objects belonging to both the known and unknown classes.