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In this paper, we present a large-scale dataset and establish a baseline forprohibited 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 8,929prohibited items are manually annotated. It raises a brand new challenge ofoverlapping image data, meanwhile shares the same properties with existingdatasets, including complex yet meaningless contexts and class imbalance. Wepropose an approach named class-balanced hierarchical refinement (CHR) to dealwith these difficulties. CHR assumes that each input image is sampled from amixture distribution, and that deep networks require an iterative process toinfer image contents accurately. To accelerate, we insert reversed connectionsto different network backbones, delivering high-level visual cues to assistmid-level features. In addition, a class-balanced loss function is designed tomaximally alleviate the noise introduced by easy negative samples. We evaluateCHR on SIXray with different ratios of positive/negative samples. Compared tothe baselines, CHR enjoys a better ability of discriminating objects especiallyusing mid-level features, which offers the possibility of using aweakly-supervised approach towards accurate object localization. In particular,the advantage of CHR is more significant in the scenarios with fewer positivetraining samples, which demonstrates its potential application in real-worldsecurity inspection.

SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images