  Recently, progress has been made in the supervised training of Convolutional
Object Detectors (e.g. Faster R-CNN) for threat recognition in carry-on luggage
using X-ray images. This is part of the Transportation Security
Administration's (TSA's) mission to protect air travelers in the United States.
While more training data with threats may reliably improve performance for this
class of deep algorithm, it is expensive to stage in realistic contexts. By
contrast, data from the real world can be collected quickly with minimal cost.
In this paper, we present a semi-supervised approach for threat recognition
which we call Background Adaptive Faster R-CNN. This approach is a training
method for two-stage object detectors which uses Domain Adaptation methods from
the field of deep learning. The data sources described earlier make two
"domains": a hand-collected data domain of images with threats, and a
real-world domain of images assumed without threats. Two domain discriminators,
one for discriminating object proposals and one for image features, are
adversarially trained to prevent encoding domain-specific information. Without
this penalty a Convolutional Neural Network (CNN) can learn to identify domains
based on superficial characteristics, and minimize a supervised loss function
without improving its ability to recognize objects. For the hand-collected
data, only object proposals and image features from backgrounds are used. The
losses for these domain-adaptive discriminators are added to the Faster R-CNN
losses of images from both domains. This can reduce threat detection false
alarm rates by matching the statistics of extracted features from
hand-collected backgrounds to real world data. Performance improvements are
demonstrated on two independently-collected datasets of labeled threats.
