  Automatic security inspection using computer vision technology is a
challenging task in real-world scenarios due to various factors, including
intra-class variance, class imbalance, and occlusion. Most of the previous
methods rarely solve the cases that the prohibited items are deliberately
hidden in messy objects due to the lack of large-scale datasets, restricted
their applications in real-world scenarios. Towards real-world prohibited item
detection, we collect a large-scale dataset, named as PIDray, which covers
various cases in real-world scenarios for prohibited item detection, especially
for deliberately hidden items. With an intensive amount of effort, our dataset
contains $12$ categories of prohibited items in $47,677$ X-ray images with
high-quality annotated segmentation masks and bounding boxes. To the best of
our knowledge, it is the largest prohibited items detection dataset to date.
Meanwhile, we design the selective dense attention network (SDANet) to
construct a strong baseline, which consists of the dense attention module and
the dependency refinement module. The dense attention module formed by the
spatial and channel-wise dense attentions, is designed to learn the
discriminative features to boost the performance. The dependency refinement
module is used to exploit the dependencies of multi-scale features. Extensive
experiments conducted on the collected PIDray dataset demonstrate that the
proposed method performs favorably against the state-of-the-art methods,
especially for detecting the deliberately hidden items.
