  Automated systems designed for screening contraband items from the X-ray
imagery are still facing difficulties with high clutter, concealment, and
extreme occlusion. In this paper, we addressed this challenge using a novel
multi-scale contour instance segmentation framework that effectively identifies
the cluttered contraband data within the baggage X-ray scans. Unlike standard
models that employ region-based or keypoint-based techniques to generate
multiple boxes around objects, we propose to derive proposals according to the
hierarchy of the regions defined by the contours. The proposed framework is
rigorously validated on three public datasets, dubbed GDXray, SIXray, and
OPIXray, where it outperforms the state-of-the-art methods by achieving the
mean average precision score of 0.9779, 0.9614, and 0.8396, respectively.
Furthermore, to the best of our knowledge, this is the first contour instance
segmentation framework that leverages multi-scale information to recognize
cluttered and concealed contraband data from the colored and grayscale security
X-ray imagery.
