# Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery

## TL;DR

This paper introduces a dual CNN architecture for automatic object-wise anomaly detection in cluttered X-ray security images, achieving high object localization accuracy but moderate anomaly classification performance.

## Contribution

It presents a novel dual CNN framework combining object localization and anomaly detection tailored for complex security X-ray imagery.

## Key findings

- Object localization achieves 97.9% mAP on six-class detection.
- Anomaly detection within objects reaches 66% accuracy.
- Demonstrates the challenge of anomaly detection in cluttered X-ray images.

## Abstract

X-ray baggage security screening is widely used to maintain aviation and transport security. Of particular interest is the focus on automated security X-ray analysis for particular classes of object such as electronics, electrical items, and liquids. However, manual inspection of such items is challenging when dealing with potentially anomalous items. Here we present a dual convolutional neural network (CNN) architecture for automatic anomaly detection within complex security X-ray imagery. We leverage recent advances in region-based (R-CNN), mask-based CNN (Mask R-CNN) and detection architectures such as RetinaNet to provide object localisation variants for specific object classes of interest. Subsequently, leveraging a range of established CNN object and fine-grained category classification approaches we formulate within object anomaly detection as a two-class problem (anomalous or benign). While the best performing object localisation method is able to perform with 97.9% mean average precision (mAP) over a six-class X-ray object detection problem, subsequent two-class anomaly/benign classification is able to achieve 66% performance for within object anomaly detection. Overall, this performance illustrates both the challenge and promise of object-wise anomaly detection within the context of cluttered X-ray security imagery.