Detection of concealed cars in complex cargo X-ray imagery using Deep Learning

TL;DR

This paper presents a deep learning-based method for detecting concealed cars in complex cargo X-ray images, achieving high accuracy and robustness against obfuscation, with potential for real-world deployment.

Contribution

The authors introduce a trained-from-scratch CNN with an oversampling scheme to effectively detect cars in challenging X-ray cargo images, even when partially obscured.

Findings

Achieved 100% classification rate at 1-in-454 false positives

Successfully detected partially obscured cars

Method is suitable for field deployment

Abstract

Non-intrusive inspection systems based on X-ray radiography techniques are routinely used at transport hubs to ensure the conformity of cargo content with the supplied shipping manifest. As trade volumes increase and regulations become more stringent, manual inspection by trained operators is less and less viable due to low throughput. Machine vision techniques can assist operators in their task by automating parts of the inspection workflow. Since cars are routinely involved in trafficking, export fraud, and tax evasion schemes, they represent an attractive target for automated detection and flagging for subsequent inspection by operators. In this contribution, we describe a method for the detection of cars in X-ray cargo images based on trained-from-scratch Convolutional Neural Networks. By introducing an oversampling scheme that suitably addresses the low number of car images available for training, we achieved 100% car image classification rate for a false positive rate of 1-in-454. Cars that were partially or completely obscured by other goods, a modus operandi frequently adopted by criminals, were correctly detected. We believe that this level of performance suggests that the method is suitable for deployment in the field. It is expected that the generic object detection workflow described can be extended to other object classes given the availability of suitable training data.