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

TL;DR

This paper introduces SIXray, a large-scale X-ray image dataset for prohibited item detection, and proposes CHR, a novel method that improves detection accuracy especially with limited positive samples, addressing challenges like overlapping images and class imbalance.

Contribution

The paper provides the SIXray dataset and a new class-balanced hierarchical refinement (CHR) approach for better prohibited item detection in security X-ray images.

Findings

CHR outperforms baseline methods in discriminating objects.

CHR is particularly effective with fewer positive samples.

The dataset and method facilitate weakly-supervised object localization.

Abstract

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.