  X-ray baggage security screening is in widespread use and crucial to
maintaining transport security for threat/anomaly detection tasks. The
automatic detection of anomaly, which is concealed within cluttered and complex
electronics/electrical items, using 2D X-ray imagery is of primary interest in
recent years. We address this task by introducing joint object sub-component
level segmentation and classification strategy using deep Convolution Neural
Network architecture. The performance is evaluated over a dataset of cluttered
X-ray baggage security imagery, consisting of consumer electrical and
electronics items using variants of dual-energy X-ray imagery (pseudo-colour,
high, low, and effective-Z). The proposed joint sub-component level
segmentation and classification approach achieve ~99% true positive and ~5%
false positive for anomaly detection task.
