  Security inspection often deals with a piece of baggage or suitcase where
objects are heavily overlapped with each other, resulting in an unsatisfactory
performance for prohibited items detection in X-ray images. In the literature,
there have been rare studies and datasets touching this important topic. In
this work, we contribute the first high-quality object detection dataset for
security inspection, named Occluded Prohibited Items X-ray (OPIXray) image
benchmark. OPIXray focused on the widely-occurred prohibited item "cutter",
annotated manually by professional inspectors from the international airport.
The test set is further divided into three occlusion levels to better
understand the performance of detectors. Furthermore, to deal with the
occlusion in X-ray images detection, we propose the De-occlusion Attention
Module (DOAM), a plug-and-play module that can be easily inserted into and thus
promote most popular detectors. Despite the heavy occlusion in X-ray imaging,
shape appearance of objects can be preserved well, and meanwhile different
materials visually appear with different colors and textures. Motivated by
these observations, our DOAM simultaneously leverages the different appearance
information of the prohibited item to generate the attention map, which helps
refine feature maps for the general detectors. We comprehensively evaluate our
module on the OPIXray dataset, and demonstrate that our module can consistently
improve the performance of the state-of-the-art detection methods such as SSD,
FCOS, etc, and significantly outperforms several widely-used attention
mechanisms. In particular, the advantages of DOAM are more significant in the
scenarios with higher levels of occlusion, which demonstrates its potential
application in real-world inspections. The OPIXray benchmark and our model are
released at https://github.com/OPIXray-author/OPIXray.
