Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection

TL;DR

This paper introduces a large high-quality X-ray security image dataset and a novel Lateral Inhibition Module to improve prohibited items detection, achieving state-of-the-art results in real-world security inspection scenarios.

Contribution

The work provides the first high-quality dataset for prohibited items detection and proposes a biologically inspired module to enhance detection accuracy in complex X-ray images.

Findings

The dataset contains over 102,000 annotated prohibited items.

The proposed LIM improves detection performance over existing methods.

The method outperforms state-of-the-art detection algorithms on benchmark datasets.

Abstract

Prohibited items detection in X-ray images often plays an important role in protecting public safety, which often deals with color-monotonous and luster-insufficient objects, resulting in unsatisfactory performance. Till now, there have been rare studies touching this topic due to the lack of specialized high-quality datasets. In this work, we first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102,928 common prohibited items of 8 categories. It is the largest dataset of high quality for prohibited items detection, gathered from the real-world airport security inspection and annotated by professional security inspectors. Besides, for accurate prohibited item detection, we further propose the Lateral Inhibition Module (LIM) inspired by the fact that humans recognize these items by ignoring irrelevant information and focusing on identifiable characteristics, especially when objects are overlapped with each other. Specifically, LIM, the elaborately designed flexible additional module, suppresses the noisy information flowing maximumly by the Bidirectional Propagation (BP) module and activates the most identifiable charismatic, boundary, from four directions by Boundary Activation (BA) module. We evaluate our method extensively on HiXray and OPIXray and the results demonstrate that it outperforms SOTA detection methods.