  Security inspection is X-ray scanning for personal belongings in suitcases,
which is significantly important for the public security but highly
time-consuming for human inspectors. Fortunately, deep learning has greatly
promoted the development of computer vision, offering a possible way of
automatic security inspection. However, items within a luggage are randomly
overlapped resulting in noisy X-ray images with heavy occlusions. Thus,
traditional CNN-based models trained through common image recognition datasets
fail to achieve satisfactory performance in this scenario. To address these
problems, we contribute the first high-quality prohibited X-ray object
detection dataset named OPIXray, which contains 8885 X-ray images from 5
categories of the widely-occurred prohibited item ``cutters''. The images are
gathered from an airport and these prohibited items are annotated manually by
professional inspectors, which can be used as a benchmark for model training
and further facilitate future research. To better improve occluded X-ray object
detection, we further propose an over-sampling de-occlusion attention network
(DOAM-O), which consists of a novel de-occlusion attention module and a new
over-sampling training strategy. Specifically, our de-occlusion module, namely
DOAM, simultaneously leverages the different appearance information of the
prohibited items; the over-sampling training strategy forces the model to put
more emphasis on these hard samples consisting these items of high occlusion
levels, which is more suitable for this scenario. We comprehensively evaluated
DOAM-O on the OPIXray dataset, which proves that our model can stably improve
the performance of the famous detection models such as SSD, YOLOv3, and FCOS,
and outperform many extensively-used attention mechanisms.
