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Prohibited items detection in X-ray images often plays an important role inprotecting public safety, which often deals with color-monotonous andluster-insufficient objects, resulting in unsatisfactory performance. Till now,there have been rare studies touching this topic due to the lack of specializedhigh-quality datasets. In this work, we first present a High-quality X-ray(HiXray) security inspection image dataset, which contains 102,928 commonprohibited items of 8 categories. It is the largest dataset of high quality forprohibited items detection, gathered from the real-world airport securityinspection and annotated by professional security inspectors. Besides, foraccurate prohibited item detection, we further propose the Lateral InhibitionModule (LIM) inspired by the fact that humans recognize these items by ignoringirrelevant information and focusing on identifiable characteristics, especiallywhen objects are overlapped with each other. Specifically, LIM, the elaboratelydesigned flexible additional module, suppresses the noisy information flowingmaximumly by the Bidirectional Propagation (BP) module and activates the mostidentifiable charismatic, boundary, from four directions by Boundary Activation(BA) module. We evaluate our method extensively on HiXray and OPIXray and theresults demonstrate that it outperforms SOTA detection methods.

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