Detection of concealed cars in complex cargo X-ray imagery using Deep Learning
Nicolas Jaccard, Thomas W. Rogers, Edward J. Morton, Lewis D. Griffin

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…
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