  In this paper, we present a large-scale dataset and establish a baseline for
prohibited item discovery in Security Inspection X-ray images. Our dataset,
named SIXray, consists of 1,059,231 X-ray images, in which 6 classes of 8,929
prohibited items are manually annotated. It raises a brand new challenge of
overlapping image data, meanwhile shares the same properties with existing
datasets, including complex yet meaningless contexts and class imbalance. We
propose an approach named class-balanced hierarchical refinement (CHR) to deal
with these difficulties. CHR assumes that each input image is sampled from a
mixture distribution, and that deep networks require an iterative process to
infer image contents accurately. To accelerate, we insert reversed connections
to different network backbones, delivering high-level visual cues to assist
mid-level features. In addition, a class-balanced loss function is designed to
maximally alleviate the noise introduced by easy negative samples. We evaluate
CHR on SIXray with different ratios of positive/negative samples. Compared to
the baselines, CHR enjoys a better ability of discriminating objects especially
using mid-level features, which offers the possibility of using a
weakly-supervised approach towards accurate object localization. In particular,
the advantage of CHR is more significant in the scenarios with fewer positive
training samples, which demonstrates its potential application in real-world
security inspection.
