  Automated detection of contraband items in X-ray images can significantly
increase public safety, by enhancing the productivity and alleviating the
mental load of security officers in airports, subways, customs/post offices,
etc. The large volume and high throughput of passengers, mailed parcels, etc.,
during rush hours make it a Big Data analysis task. Modern computer vision
algorithms relying on Deep Neural Networks (DNNs) have proven capable of
undertaking this task even under resource-constrained and embedded execution
scenarios, e.g., as is the case with fast, single-stage, anchor-based object
detectors. This paper proposes a two-fold improvement of such algorithms for
the X-ray analysis domain, introducing two complementary novelties. Firstly,
more efficient anchors are obtained by hierarchical clustering the sizes of the
ground-truth training set bounding boxes; thus, the resulting anchors follow a
natural hierarchy aligned with the semantic structure of the data. Secondly,
the default Non-Maximum Suppression (NMS) algorithm at the end of the object
detection pipeline is modified to better handle occluded object detection and
to reduce the number of false predictions, by inserting the Efficient
Intersection over Union (E-IoU) metric into the Weighted Cluster NMS method.
E-IoU provides more discriminative geometrical correlations between the
candidate bounding boxes/Regions-of-Interest (RoIs). The proposed method is
implemented on a common single-stage object detector (YOLOv5) and its
experimental evaluation on a relevant public dataset indicates significant
accuracy gains over both the baseline and competing approaches. This highlights
the potential of Big Data analysis in enhancing public safety.

Computer Vision and Pattern Recognition